Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It mentions 'analyze its impact' and 'complete lifecycle management' but doesn't specify what analysis entails, what lifecycle stages are created, whether this is a write operation, permissions needed, or response format. For a 10-parameter mutation tool with no annotations, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that efficiently lists the tool's core functions. It's front-loaded with the primary action ('add a new feature') and avoids redundancy. However, it could be more concise by removing 'or project' redundancy with 'PRD' and clarifying the relationship between analysis and task expansion.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (10 parameters, 6 required, mutation tool), lack of annotations, and no output schema, the description is incomplete. It doesn't cover parameter details, behavioral expectations, error conditions, or return values. For a tool with significant scope ('analyze impact', 'expand into tasks', 'lifecycle management'), this leaves too much unspecified for reliable agent invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for all 10 parameters. It mentions 'feature', 'PRD or project', 'impact analysis', 'actionable tasks', and 'lifecycle management', which loosely map to some parameters like 'featureIdea', 'targetPRD', 'targetProject', 'expandToTasks', and 'createLifecycle'. However, it doesn't explain the purpose or format of critical parameters like 'autoApprove', 'businessJustification', 'targetUsers', or 'requestedBy', leaving most parameters inadequately documented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('add', 'analyze', 'expand') and resources ('feature', 'PRD or project', 'actionable tasks', 'lifecycle management'). It distinguishes from siblings like 'enhance_prd' or 'create_issue' by focusing on feature addition with analysis and task expansion. However, it doesn't explicitly differentiate from 'expand_task' or 'generate_prd' in sibling context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for project/issue management (e.g., 'create_issue', 'enhance_prd', 'expand_task'), there's no indication of prerequisites, appropriate contexts, or exclusions. The agent must infer usage from the description alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states the action is 'Add' (implying a write/mutation operation) but doesn't disclose critical traits: whether it requires specific permissions, if it's idempotent (adding duplicate issues), what happens on failure (partial adds), rate limits, or response format. The description doesn't contradict annotations (none exist), but it's insufficient for a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, direct sentence with zero wasted words—front-loaded and perfectly concise. It efficiently communicates the core action without unnecessary elaboration, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a mutation tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It lacks details on behavior, error handling, parameter semantics, and integration with sibling tools (e.g., 'get_current_sprint'). For a tool that modifies data, this minimal description risks misuse or confusion by the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate but adds no parameter details. It doesn't explain what 'sprintId' and 'issueIds' represent (e.g., numeric IDs, strings), their format, sourcing (from 'list_sprints' or 'list_issues'), or constraints (e.g., max issues per call). The description repeats the tool name without enhancing parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Add') and target ('issues to an existing sprint'), making the purpose immediately understandable. It distinguishes from sibling tools like 'create_sprint' (creates new sprints) and 'remove_issues_from_sprint' (removes issues). However, it doesn't specify what type of 'issues' (e.g., tasks, bugs) or mention the resource context (e.g., project management system).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., sprint must exist, issues must be unassigned), exclusions (e.g., cannot add issues to completed sprints), or related tools like 'create_issue' (to first create issues) or 'plan_sprint' (for broader sprint planning). Usage is implied but not explicitly defined.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states the action is 'Add' (implying a write operation) but doesn't disclose permissions required, rate limits, whether the operation is idempotent, or what happens on success/failure. For a mutation tool with zero annotation coverage, this is inadequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with zero wasted words. It's front-loaded with the core action and target, making it easy to parse. Every word earns its place, though it could benefit from additional context.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with 3 required parameters, 0% schema coverage, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'adding an item' entails operationally, what the parameters mean, or what to expect upon completion. The agent lacks critical context for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate but adds no parameter information. It doesn't explain what 'projectId', 'contentId', or 'contentType' represent, their formats, or relationships. The enum for 'contentType' is documented in the schema but not explained in the description, leaving semantics unclear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Add') and target ('an item to a GitHub project'), making the purpose immediately understandable. It distinguishes from siblings like 'create_project' or 'update_project' by focusing on adding items to existing projects. However, it doesn't specify what types of items can be added beyond what's implied by the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'add_issues_to_sprint' or 'create_issue'. It doesn't mention prerequisites (e.g., needing an existing project) or exclusions. The agent must infer usage from the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'AI-powered analysis' which implies computational processing, but doesn't describe output format, potential latency, rate limits, authentication needs, or whether it's read-only vs. mutative. For an 8-parameter analysis tool with zero annotation coverage, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that packs multiple analysis components. It's appropriately sized for the tool's complexity and front-loads the core purpose. No wasted words, though it could benefit from more structural separation of the different analysis aspects.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (8 parameters, 6 required), no annotations, no output schema, and 0% schema description coverage, the description is inadequate. It mentions analysis components but doesn't explain what the analysis produces, how parameters interact, or what the agent can expect as results. For an AI analysis tool with rich inputs, this leaves too many unanswered questions.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so all 8 parameters are undocumented in the schema. The description mentions 'task complexity, effort estimation, risk assessment, and actionable recommendations' which partially maps to some parameters (e.g., taskTitle, taskDescription, includeRisks, includeRecommendations), but doesn't explain parameter purposes, formats, or relationships. It fails to compensate for the complete lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs AI-powered analysis of task complexity with specific components: effort estimation, risk assessment, and actionable recommendations. It uses specific verbs ('analyze', 'estimate', 'assess', 'provide') and identifies the resource (task complexity). However, it doesn't explicitly differentiate from sibling tools like 'expand_task' or 'get_next_task' that might also involve task analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for task/project management (e.g., 'expand_task', 'plan_sprint', 'get_next_task'), there's no indication of appropriate contexts, prerequisites, or exclusions. The description only states what it does, not when it should be used.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Create a new GitHub issue' implies a write operation, but it doesn't disclose any behavioral traits such as required permissions, rate limits, whether it's idempotent, what happens on failure, or the format of the response. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste. It's front-loaded with the core action ('Create a new GitHub issue'), making it immediately clear. Every word earns its place, and there's no unnecessary elaboration or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (a mutation tool with 7 parameters, 4 required), lack of annotations, and no output schema, the description is incomplete. It doesn't provide enough context for safe and effective use—missing details on behavior, parameters, and expected outcomes. The agent would struggle to invoke this tool correctly without additional information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, meaning none of the 7 parameters have descriptions in the schema. The tool description adds no information about parameters beyond what's implied by the tool name (e.g., 'title' and 'description' might be guessed). It doesn't explain what 'milestoneId', 'assignees', 'labels', 'priority', or 'type' mean or how to format them, failing to compensate for the low schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Create a new GitHub issue' clearly states the verb ('Create') and resource ('GitHub issue'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'add_issues_to_sprint' or 'update_issue', which also involve GitHub issue operations, so it doesn't reach the highest clarity level.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing repository access), when not to use it (e.g., for modifying existing issues), or point to sibling tools like 'update_issue' for different scenarios. The agent must infer usage from the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Create a new GitHub label' which implies a write operation, but doesn't disclose permissions needed, rate limits, whether it's idempotent, or what happens on duplicate names. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It's appropriately sized for a simple creation tool and front-loads the essential information. Every word earns its place in conveying the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given this is a mutation tool with 3 parameters, 0% schema description coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, error conditions, or behavioral details needed for safe invocation. The description alone is insufficient for an agent to use this tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must compensate but provides no parameter information. It doesn't explain what 'name', 'color', or 'description' parameters represent, their formats (e.g., color as hex code), or constraints. With 3 parameters and no schema descriptions, the description adds no semantic value beyond what's inferred from the tool name.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Create') and resource ('GitHub label'), making the purpose immediately understandable. It distinguishes from sibling tools like 'list_labels' by specifying creation rather than listing. However, it doesn't explicitly differentiate from other creation tools like 'create_issue' or 'create_milestone' beyond the resource type.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites (e.g., repository access), when not to use it (e.g., for existing labels), or comparisons to sibling tools like 'update_issue' for modifying labels. Usage is implied but not explicitly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Create a new milestone' implies a write/mutation operation but provides no information about permissions required, whether the creation is reversible, what happens on success/failure, rate limits, or any side effects. For a mutation tool with zero annotation coverage, this is completely inadequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise at just three words, which could be appropriate if it were more informative. However, this conciseness comes at the cost of under-specification rather than efficient communication. The structure is simple but lacks the necessary information density for a tool with multiple parameters and sibling alternatives.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a mutation tool with 3 parameters, no annotations, no output schema, and multiple sibling alternatives, yet the description provides minimal information. It fails to explain what a milestone is in this context, how it differs from other project management artifacts, what the parameters mean, or what happens after creation. For a tool of this complexity and context, the description is completely inadequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description provides zero information about parameters. With 3 parameters (title, description, dueDate) and 0% schema description coverage, the schema only shows data types without explaining what these fields represent or their constraints. The description doesn't mention any parameters, their purposes, or provide any semantic context beyond what's in the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Create a new milestone' is a tautology that merely restates the tool name without adding specificity. It doesn't distinguish this tool from sibling tools like 'create_project', 'create_sprint', or 'create_roadmap' that also create different resources. While the verb 'create' is clear, the description fails to provide meaningful differentiation from other creation tools in the server.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides absolutely no guidance about when to use this tool versus alternatives. With multiple sibling creation tools (create_project, create_sprint, create_roadmap, create_issue, etc.), there's no indication of when a milestone is appropriate versus other project management artifacts. No prerequisites, context, or exclusions are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Create a new GitHub project' which implies a write operation, but doesn't disclose any behavioral traits such as required permissions, rate limits, whether it's idempotent, what happens on duplicate titles, or what the response looks like. This is a significant gap for a mutation tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it immediately scannable and appropriately sized for its purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given this is a mutation tool with 4 parameters (3 required), 0% schema description coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain parameters, behavioral expectations, or return values, leaving significant gaps for an AI agent to understand and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 4 parameters with 0% description coverage, meaning none have descriptions in the schema. The tool description provides no information about parameters, not even mentioning what they are or their purposes (title, shortDescription, owner, visibility). This fails to compensate for the schema's lack of documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Create') and resource ('new GitHub project'), making the purpose immediately understandable. It distinguishes from siblings like 'update_project' or 'delete_project' by specifying creation. However, it doesn't explicitly differentiate from other creation tools like 'create_issue' or 'create_milestone' beyond the resource name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., authentication, repository context), when not to use it, or how it differs from similar creation tools like 'create_issue' or 'create_milestone' in the sibling list.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It states 'Create' which implies a write/mutation operation, but doesn't disclose permission requirements, rate limits, whether the operation is idempotent, what happens on failure, or the response format. This is a significant gap for a mutation tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with zero wasted words. It's appropriately sized for a basic tool definition and front-loads the essential information (create custom field). Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with 6 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, error conditions, or behavioral aspects. The description should provide more context about the creation operation, parameter meanings, and expected outcomes.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage for 6 parameters, the description provides no parameter information beyond what's inferred from the tool name. It doesn't explain what 'projectId', 'name', 'type', 'options', 'description', or 'required' mean, their formats, constraints, or relationships. The description fails to compensate for the complete lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Create') and resource ('a custom field for a GitHub project'), making the purpose immediately understandable. It distinguishes from siblings like 'update_project_field' by specifying creation rather than modification, though it doesn't explicitly contrast with 'list_project_fields' or other field-related tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing project), when not to use it (e.g., for updating existing fields), or direct alternatives like 'update_project_field' for modifications or 'list_project_fields' for viewing existing fields.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. 'Create' implies a write/mutation operation, but the description doesn't disclose any behavioral traits: no information about permissions required, whether this is idempotent, what happens on duplicate names, rate limits, or what the response contains. For a mutation tool with zero annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that gets straight to the point with no wasted words. It's appropriately sized for a tool with a clear primary function, though the brevity comes at the cost of completeness. Every word earns its place in conveying the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with 3 required parameters, 0% schema description coverage, no annotations, and no output schema, the description is incomplete. It states what the tool does at a high level but provides none of the contextual information needed for effective use: no parameter explanations, no behavioral context, no usage guidance, and no indication of what the tool returns. The agent would need to guess about many aspects of tool behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate by explaining parameters, but it provides no parameter information whatsoever. The three required parameters (projectId, name, layout) are completely undocumented in the description, and the layout enum values (board, table, timeline, roadmap) aren't mentioned or explained. The description adds zero value beyond what the bare schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Create a new view') and target resource ('for a GitHub project'), making the purpose immediately understandable. It distinguishes from siblings like 'create_project' or 'update_project_view' by specifying it's about views rather than projects themselves or updates to existing views. However, it doesn't specify what a 'view' entails in GitHub projects context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing project), differentiate from similar tools like 'create_project' or 'update_project_view', or indicate when this operation is appropriate versus other view-related operations. The agent must infer usage from the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. While 'create' implies a write operation, the description doesn't address permissions needed, whether this is an atomic operation, what happens on failure, or what the response contains. For a complex creation tool with nested objects, this leaves significant behavioral questions unanswered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a tool description and gets straight to the point with zero wasted text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex creation tool with nested parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what constitutes a successful creation, what gets returned, error conditions, or how this tool relates to the many sibling tools in the project management domain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage and 2 complex nested parameters, the description provides minimal parameter guidance. It mentions 'milestones and tasks' which hints at the 'milestones' parameter structure, but offers no insight into the 'project' parameter or the specific structure expected for either parameter. The description doesn't compensate for the schema's lack of descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('create') and resource ('project roadmap with milestones and tasks'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from similar siblings like 'create_project' or 'create_milestone', which reduces its differentiation value.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools for creating projects, milestones, and other project management entities, there's no indication of when this comprehensive roadmap creation tool is preferred over more granular alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It states 'create' implies a write operation, but doesn't disclose behavioral traits like required permissions, whether it's idempotent, what happens on conflicts, or error handling. This leaves significant gaps for a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words. It's front-loaded and appropriately sized for the tool's purpose, making it easy to scan and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a 5-parameter mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavior, parameter usage, and return values, making it insufficient for an AI agent to use the tool effectively without additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate but adds no parameter information. It doesn't explain what 'issueIds' should contain, date formats for 'startDate' and 'endDate', or constraints on 'title' and 'description'. With 5 undocumented parameters, this is inadequate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('create') and resource ('development sprint'), making the purpose evident. However, it doesn't differentiate from sibling tools like 'create_milestone' or 'create_project', which also create development artifacts, so it's not fully specific to sibling context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like 'plan_sprint' or 'create_milestone'. The description lacks context about prerequisites, such as needing an existing project or milestone, or exclusions for overlapping functionality with siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Create' implies a write/mutation operation, the description doesn't specify what gets created (file? database entry? visual artifact?), whether this operation is idempotent, what permissions are required, or what happens on failure. It mentions 'comprehensive' and 'full bidirectional traceability' but doesn't explain what those mean operationally.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that efficiently communicates the core purpose. Every word earns its place - 'comprehensive' sets scope, the arrow notation clearly shows relationships, and 'full bidirectional traceability' specifies a key characteristic. There's no wasted verbiage or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 8 parameters (all required), 0% schema description coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool produces (no output schema), doesn't clarify parameter purposes, and provides minimal behavioral context. While the purpose is clear, the description leaves too many operational questions unanswered for effective agent use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage for all 8 parameters, the description provides no information about what any parameter means or how they should be used. The description mentions 'PRD business requirements', 'features', 'use cases', and 'tasks' which loosely map to some parameters, but doesn't explain the purpose of 'includeUseCases', 'includeTraceabilityLinks', 'includeCoverageAnalysis', or 'validateCompleteness' parameters. The description fails to compensate for the complete lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Create a comprehensive requirements traceability matrix') and specifies the exact relationships being established ('linking PRD business requirements → features → use cases → tasks with full bidirectional traceability'). It distinguishes itself from sibling tools like 'create_project' or 'create_roadmap' by focusing specifically on traceability matrix creation rather than general project management artifacts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing existing project data), doesn't specify when this tool is appropriate versus other creation tools like 'create_project' or 'create_roadmap', and offers no exclusions or limitations. The agent must infer usage context from the tool name and parameters alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. 'Delete' implies a destructive, irreversible mutation, but the description doesn't specify permissions required, confirmation prompts, error handling, or what happens to associated issues. For a destructive tool with zero annotation coverage, this leaves critical behavioral traits unaddressed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it immediately scannable. Every word earns its place, and there's no redundancy or unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a destructive mutation tool with no annotations, 0% schema coverage, and no output schema, the description is incomplete. It lacks critical context: behavioral risks, parameter details, error cases, and output expectations. The description doesn't compensate for the missing structured data, leaving the agent under-informed about a high-stakes operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'milestone' but doesn't explain what 'milestoneId' represents (e.g., numeric ID vs. title), how to obtain it, or format constraints. With 1 parameter fully undocumented in the schema, the description adds minimal semantic value beyond the tool name.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Delete') and resource ('a GitHub milestone'), making the purpose immediately understandable. It distinguishes from siblings like 'update_milestone' and 'list_milestones' by specifying the destructive action. However, it doesn't explicitly differentiate from other deletion tools like 'delete_project', so it's not fully sibling-aware.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing milestone ID from 'list_milestones' or 'get_milestone_metrics'), exclusions, or comparisons to similar tools like 'update_milestone' for modification instead of deletion. Usage is implied but not explicitly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Delete' implies a destructive, irreversible mutation, but the description doesn't specify permissions required, whether deletion is permanent or recoverable, rate limits, or what happens to associated items (e.g., issues, milestones). For a destructive tool with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it immediately scannable. Every word ('Delete', 'a', 'GitHub', 'project') earns its place by specifying the operation and target.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's destructive nature, lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't address critical context like permissions, irreversibility, error conditions, or effects on related data. For a deletion tool in a project management context, this leaves too many unknowns for safe and effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for undocumented parameters. The description mentions 'a GitHub project', which hints that 'projectId' refers to a GitHub project identifier, but doesn't clarify format (e.g., numeric ID, URL slug) or where to find it. It adds minimal meaning beyond the schema's property name, partially compensating for the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Delete') and resource ('a GitHub project'), making the purpose immediately understandable. It distinguishes itself from sibling tools like 'update_project' or 'get_project' by specifying a destructive operation. However, it doesn't explicitly differentiate from 'delete_milestone' or other deletion tools in terms of scope or hierarchy.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing admin permissions), consequences (e.g., irreversible deletion), or when to choose 'delete_project' over other tools like 'remove_project_item' or 'update_project' with status changes. Usage is implied only by the tool name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'AI-powered improvements' but doesn't detail how the enhancement works, what permissions or inputs are needed, whether changes are reversible, or what the output format looks like. For a tool with 9 parameters and no annotations, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose. It avoids redundancy and wastes no words, though it could benefit from more structured guidance for such a complex tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (9 parameters, 7 required), lack of annotations, and no output schema, the description is incomplete. It doesn't explain how enhancements are applied, what the output entails, or handle the many parameters. For a tool with this level of complexity, more detail is needed to guide effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'adding missing elements, improving clarity, and providing comprehensive analysis,' which loosely relates to some parameters like 'addMissingElements' and 'improveExisting,' but doesn't explain the purpose or usage of the 9 parameters (e.g., 'enhancementType', 'focusAreas', 'includeResearch'). This fails to adequately clarify parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Enhance an existing PRD with AI-powered improvements, adding missing elements, improving clarity, and providing comprehensive analysis.' It specifies the verb ('enhance'), resource ('existing PRD'), and scope ('AI-powered improvements'), though it doesn't explicitly distinguish it from sibling tools like 'generate_prd' or 'parse_prd'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'generate_prd' (for creating new PRDs) or 'parse_prd' (for analyzing existing ones), nor does it specify prerequisites or exclusions for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'AI-powered analysis' and 'implementation recommendations', but doesn't clarify output format, whether it's read-only or mutating, potential rate limits, or error conditions. For a tool with 11 parameters and no annotations, this leaves significant gaps in understanding how the tool behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that efficiently conveys the core functionality. It's front-loaded with the main purpose and includes key features without unnecessary elaboration. However, given the complexity of the tool (11 parameters, no annotations), it might be too brief to be fully helpful.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 11 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It explains what the tool does at a high level but provides no guidance on usage, parameter meanings, behavioral traits, or expected outputs. The agent would struggle to use this tool effectively without additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, meaning none of the 11 parameters have descriptions in the schema. The tool description doesn't mention any parameters at all, failing to compensate for this gap. Parameters like 'currentComplexity', 'maxSubtasks', 'targetComplexity', and 'projectType' remain completely unexplained in both schema and description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Break down a complex task into smaller, manageable subtasks with AI-powered analysis, dependency detection, and implementation recommendations.' It specifies the verb ('break down'), resource ('complex task'), and key features (AI analysis, dependency detection, recommendations). However, it doesn't explicitly distinguish this from sibling tools like 'analyze_task_complexity' or 'plan_sprint', which might have overlapping functionality in task analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, ideal scenarios, or exclusions. Given the many sibling tools for task/project management (e.g., 'analyze_task_complexity', 'plan_sprint', 'create_issue'), the agent lacks context on when this specific breakdown tool is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'AI analysis and industry best practices,' which hints at generative behavior, but doesn't clarify critical aspects like whether this is a read-only or mutating operation, what permissions are needed, how long it takes, or what the output format is. For a tool with 9 parameters and no annotations, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that efficiently conveys the core purpose without unnecessary words. It's front-loaded with the main action ('Generate a comprehensive Product Requirements Document') and includes key details ('from a project idea using AI analysis and industry best practices') in a logical flow. Every part of the sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (9 parameters, 5 required), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a PRD document, a structured output), how errors are handled, or the scope of 'AI analysis.' For a generative tool with multiple inputs, more context is needed to ensure proper usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, meaning none of the 9 parameters have descriptions in the schema. The tool description doesn't mention any parameters, so it adds no semantic information beyond what's inferred from parameter names (e.g., 'projectIdea' likely contains the idea text). This fails to compensate for the lack of schema documentation, making parameter usage unclear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Generate a comprehensive Product Requirements Document (PRD) from a project idea using AI analysis and industry best practices.' It specifies the verb ('Generate'), resource ('Product Requirements Document'), and method ('using AI analysis and industry best practices'). However, it doesn't explicitly differentiate from sibling tools like 'enhance_prd' or 'parse_prd', which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'enhance_prd' (which might modify existing PRDs) or 'parse_prd' (which might analyze PRDs), nor does it specify prerequisites or exclusions. The only implied usage is for creating PRDs from project ideas, but this is too vague for effective tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a 'Get' operation, implying it's likely read-only, but doesn't confirm this or describe any other behavioral traits like authentication needs, rate limits, error conditions, or what 'active' means in this context. The description is too minimal for a tool with parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with no wasted words. It's front-loaded with the core purpose and appropriately sized for what it conveys, though it lacks necessary detail for full tool understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has one undocumented parameter, no annotations, and no output schema, the description is incomplete. It doesn't explain what 'includeIssues' does, what the return value looks like, or any behavioral context needed for proper tool invocation in a system with many sibling sprint-related tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one required parameter 'includeIssues' with 0% description coverage in the schema, and the tool description provides no information about parameters. The description doesn't mention 'includeIssues' at all, leaving the parameter completely undocumented and unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Get the currently active sprint' clearly states the verb ('Get') and resource ('currently active sprint'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_sprint_metrics' or 'list_sprints' that might also retrieve sprint information, preventing a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'get_sprint_metrics' or 'list_sprints'. It doesn't mention prerequisites, exclusions, or specific contexts where this tool is preferred, leaving the agent to infer usage from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. While it indicates this is a read operation ('Get') and specifies supported field types, it doesn't disclose important behavioral aspects like authentication requirements, rate limits, error handling, response format, or whether it requires specific permissions. The description provides basic functional context but misses critical operational details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in a single sentence that front-loads the core purpose. While concise, it could potentially benefit from slightly more detail given the complete lack of parameter documentation and behavioral context in annotations.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 3 required parameters, 0% schema description coverage, no annotations, and no output schema, the description is insufficiently complete. It explains what the tool does at a high level but fails to provide necessary context about parameter meanings, expected inputs, return values, or operational constraints that would enable effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage for 3 required parameters, the description provides no information about what projectId, itemId, or fieldId represent, their expected formats, or how to obtain them. The description mentions 'GitHub project item' and 'field types' but doesn't connect these concepts to the specific parameters, leaving significant semantic gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Get a field value') and resource ('for a GitHub project item'), and distinguishes it from siblings by specifying it supports reading all field types. This provides precise differentiation from tools like 'set_field_value' (write operation) and 'list_project_fields' (list operation).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by specifying it reads field values for project items, but doesn't explicitly state when to use this tool versus alternatives like 'get_project' or 'get_issue'. No guidance is provided about prerequisites, error conditions, or when not to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves details but does not specify whether this is a read-only operation, what permissions are required, how errors are handled, or the format of returned data. For a tool with zero annotation coverage, this leaves critical behavioral traits undocumented.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, direct sentence with no wasted words, making it easy to parse. It front-loads the core purpose ('Get details') efficiently, though this brevity contributes to gaps in other dimensions. Every word serves a clear function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations, 0% schema description coverage, and no output schema, the description is incomplete. It does not address behavioral traits, parameter details, or return values, leaving the agent with insufficient information to use the tool effectively in a complex environment with many sibling tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not explain the 'issueId' parameter beyond implying it identifies a specific issue. It lacks details on format (e.g., numeric ID, URL), validation, or examples. With one required parameter and no schema descriptions, the description fails to compensate for the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('details of a specific GitHub issue'), making the purpose immediately understandable. It distinguishes from siblings like 'list_issues' by specifying retrieval of a single issue rather than listing multiple. However, it lacks specificity about what details are included, which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'list_issues' or 'update_issue'. It does not mention prerequisites (e.g., needing an issue ID) or contextual factors, leaving the agent to infer usage from the name alone. This minimal guidance is insufficient for effective tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states it 'gets' metrics (implying read-only), but doesn't specify what 'progress metrics' includes (e.g., completion percentage, issue counts, timelines), whether it requires specific permissions, or how it handles errors. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, focused sentence with zero wasted words. It's front-loaded with the core purpose and appropriately sized for a simple retrieval tool. Every word earns its place by specifying what's being retrieved and for what resource.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 2 required parameters, 0% schema description coverage, no annotations, and no output schema, the description is insufficient. It doesn't explain what metrics are returned, what 'includeIssues' controls, or provide any behavioral context. The agent would struggle to use this effectively without additional documentation or trial-and-error.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema provides no parameter documentation. The description mentions 'specific milestone' which hints at 'milestoneId', but doesn't explain what 'includeIssues' does or why it's required. It adds minimal semantic value beyond what can be inferred from parameter names, failing to compensate for the complete lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and the resource 'progress metrics for a specific milestone', making the purpose unambiguous. It distinguishes from siblings like 'get_milestones' (list) and 'get_sprint_metrics' (different resource), though it doesn't explicitly name these alternatives. The specificity of 'progress metrics' provides good differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'get_milestones' (which might list milestones without metrics) or 'get_sprint_metrics' (for sprint-level metrics). There's no mention of prerequisites, context, or exclusion criteria. The agent must infer usage from the name and description alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions 'AI-powered recommendations' which hints at algorithmic behavior, but doesn't disclose critical traits like whether this is a read-only operation, what permissions are needed, how recommendations are generated, rate limits, or what the output format looks like. For a tool with 10 parameters and no annotations, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that efficiently conveys the core purpose. It's front-loaded with the main action and includes key criteria without unnecessary elaboration. However, it could be slightly more concise by removing redundant phrasing like 'to work on'.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (10 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the tool's behavior, parameter usage, or output format. For a recommendation tool with many inputs and no structured documentation, this leaves too many gaps for an AI agent to use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema provides no parameter documentation. The description mentions general criteria like 'priorities, dependencies, team capacity, and current project state' which loosely map to some parameters (e.g., 'teamSkills', 'sprintCapacity', 'currentPhase'), but it doesn't explain what any of the 10 parameters actually mean, their formats, or how they influence recommendations. This fails to compensate for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get AI-powered recommendations for the next task to work on' with specific criteria like priorities, dependencies, team capacity, and project state. It uses a specific verb ('Get') and resource ('recommendations'), but doesn't explicitly distinguish it from siblings like 'analyze_task_complexity' or 'plan_sprint' which might have overlapping functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, exclusions, or compare it to sibling tools like 'get_current_sprint' or 'list_issues' that might provide related information. Usage is implied by the purpose but not explicitly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states it 'gets a list', implying a read-only operation, but doesn't cover permissions, rate limits, pagination, or response format. For a tool with two required parameters and no output schema, this leaves critical behavioral traits undocumented.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste—it directly states the purpose without fluff. It's appropriately sized for a simple tool, though this conciseness comes at the cost of detail in other dimensions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (2 required parameters, no annotations, no output schema), the description is incomplete. It lacks parameter explanations, behavioral context, and output details, making it inadequate for reliable agent use without additional inference or trial-and-error.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions no parameters at all, failing to explain 'limit' (e.g., max results) or 'includeIssues' (e.g., whether to fetch related issues). This leaves the agent guessing about input semantics beyond basic types.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Get a list of overdue milestones' clearly states the verb ('Get') and resource ('overdue milestones'), but it's vague about scope and doesn't distinguish from siblings like 'list_milestones' or 'get_upcoming_milestones'. It specifies the 'overdue' filter but doesn't clarify what constitutes 'overdue' or how results are determined.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like 'list_milestones' or 'get_upcoming_milestones'. The description implies it's for retrieving overdue items, but it doesn't specify prerequisites, context, or exclusions, leaving the agent to guess based on tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it 'gets details' but doesn't clarify if this is a read-only operation, what permissions are required, whether it's idempotent, or how errors are handled (e.g., invalid project IDs). For a tool with zero annotation coverage, this leaves critical behavioral traits unspecified.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with zero wasted words—it directly states the tool's function. Every word ('Get details of a specific GitHub project') earns its place by clarifying scope and resource, making it highly efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 parameter, no nested objects) but lack of annotations and output schema, the description is incomplete. It doesn't explain what 'details' include in the return value, error conditions, or dependencies on other tools (e.g., 'projectId' from 'list_projects'). For a read operation with no structured output documentation, more context is needed to guide effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 1 parameter ('projectId') with 0% description coverage, so the schema provides no semantic context. The description implies the parameter identifies 'a specific GitHub project' but doesn't specify format (e.g., numeric ID, URL) or sourcing (e.g., from 'list_projects'). This adds minimal meaning beyond the schema's structural definition, aligning with the baseline for low coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('details of a specific GitHub project'), making the purpose unambiguous. It distinguishes from siblings like 'list_projects' (which returns multiple projects) by specifying retrieval of a single project's details. However, it doesn't specify what 'details' include (e.g., fields, metadata), which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a project ID), contrast with 'list_projects' for browsing, or specify scenarios like retrieving metadata for editing. Without such context, the agent must infer usage from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Get progress metrics' which implies a read operation, but it doesn't cover aspects like authentication needs, rate limits, error handling, or what the output looks like (e.g., metrics format, whether it's paginated). This is a significant gap for a tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that is front-loaded with the core purpose. There is no wasted text, and it's appropriately sized for a simple tool, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (a metrics tool with 2 parameters), no annotations, no output schema, and 0% schema coverage, the description is incomplete. It doesn't explain return values, parameter details, or behavioral traits, making it inadequate for effective tool selection and invocation by an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, so parameters 'sprintId' and 'includeIssues' are undocumented in the schema. The description adds no meaning beyond the tool's purpose; it doesn't explain what 'sprintId' should be (e.g., format, source) or what 'includeIssues' entails (e.g., whether it adds issue details to metrics). With low schema coverage, the description fails to compensate adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and the resource 'progress metrics for a specific sprint', making the purpose understandable. However, it doesn't distinguish this tool from sibling tools like 'get_current_sprint' or 'get_milestone_metrics', which might also retrieve sprint-related data, so it lacks sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, such as how it differs from 'get_current_sprint' or other metrics tools in the sibling list, leaving usage unclear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, or what the output format looks like. This is a significant gap for a tool with parameters and no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words. It is front-loaded with the core purpose, making it easy to scan and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (3 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain return values, error conditions, or behavioral traits, leaving the agent with insufficient information to use the tool effectively beyond basic invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate by explaining parameters. It mentions 'within a time frame', which loosely relates to 'daysAhead', but doesn't clarify the meaning of 'limit' or 'includeIssues'. This adds minimal value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('upcoming milestones'), specifying the scope ('within a time frame'). It distinguishes from siblings like 'list_milestones' by focusing on upcoming items, though it doesn't explicitly mention how it differs from 'get_overdue_milestones' or 'get_milestone_metrics'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as 'list_milestones' or 'get_overdue_milestones'. The description implies usage for time-based queries but lacks explicit context, prerequisites, or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'List GitHub issues' implies a read-only operation but does not specify critical traits like pagination behavior, rate limits, authentication requirements, error handling, or return format. For a tool with 7 parameters and no output schema, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with just three words, making it front-loaded and waste-free. Every word ('List', 'GitHub', 'issues') contributes directly to the core purpose, though this brevity comes at the cost of completeness. It efficiently communicates the basic intent without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (7 parameters, 1 required), lack of annotations, and no output schema, the description is incomplete. It does not address parameter meanings, behavioral traits, or output expectations, leaving significant gaps for the agent to navigate. For a listing tool with multiple filtering options, more context is needed to be adequately helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 7 parameters with 0% description coverage, and the tool description provides no information about any parameters. It does not explain what 'status', 'milestone', 'labels', etc., mean, their expected formats, or how they affect the listing. With low schema coverage and no compensation in the description, this fails to add value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'List GitHub issues' clearly states the verb ('List') and resource ('GitHub issues'), providing a basic understanding of the tool's function. However, it lacks specificity about scope (e.g., repository, organization) and does not differentiate from sibling tools like 'get_issue' (which fetches a single issue) or 'list_project_items' (which might include issues). This makes it vague but not tautological.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It does not mention context such as filtering needs, comparison to 'get_issue' for single issues, or prerequisites like repository selection. Without any implied or explicit usage instructions, it leaves the agent to infer based on the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It states it lists labels but doesn't disclose behavioral traits like pagination, rate limits, authentication needs, or what 'all' entails (e.g., across repos or orgs). This is a significant gap for a list operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste. It's front-loaded and appropriately sized for a simple tool, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (one optional parameter) but lack of annotations and output schema, the description is incomplete. It doesn't explain return values, error handling, or scope, leaving gaps for an AI agent to use it correctly in context with siblings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one parameter ('limit') with 0% description coverage. The description adds no information about parameters, so it doesn't compensate for the schema gap. However, with only one parameter, the baseline is moderate, but the lack of any param info keeps it at a minimal viable level.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('List') and resource ('all GitHub labels'), making the purpose immediately understandable. It doesn't differentiate from sibling tools like 'create_label' or other list tools, but it's specific enough to know what it does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'list_issues' and 'list_milestones', there's no indication of context or prerequisites for listing labels specifically.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully disclose behavioral traits. 'List milestones' implies a read-only operation but offers no details on permissions, rate limits, pagination, error handling, or what the output contains. For a tool with parameters and no output schema, this lack of behavioral context is inadequate and leaves critical operational aspects unspecified.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with only two words, making it front-loaded and free of unnecessary verbiage. While under-specified, it does not waste space on redundant or irrelevant information, adhering strictly to a minimalistic structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (3 parameters, 1 required, no schema descriptions, no output schema, and no annotations), the description is severely incomplete. It does not address parameter meanings, behavioral expectations, output format, or differentiation from siblings. For a listing tool with filtering capabilities, this minimal description fails to provide the necessary context for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, meaning none of the three parameters (status, sort, direction) are documented in the schema. The description 'List milestones' adds no information about these parameters—it does not explain what 'status' represents, what sorting options exist, or how 'direction' affects results. This fails to compensate for the complete lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'List milestones' is a tautology that restates the tool name without adding meaningful context. It specifies the verb 'list' and resource 'milestones' but lacks any distinguishing details about scope, filtering, or how it differs from sibling tools like 'get_milestone_metrics', 'get_overdue_milestones', or 'get_upcoming_milestones'. This minimal statement fails to clarify what specific aspect of milestones is being listed.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It does not mention any context, prerequisites, or exclusions, nor does it reference sibling tools that might serve similar purposes (e.g., 'get_overdue_milestones' for filtered lists). Without such information, an agent cannot determine appropriate usage scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but only states it lists fields without disclosing behavioral traits like pagination, rate limits, error handling, or response format. It mentions 'all fields' but doesn't clarify if this includes archived or hidden fields, leaving gaps in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and appropriately sized for a simple list operation, earning full marks for conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (listing fields in a project), no annotations, no output schema, and low parameter coverage, the description is incomplete. It lacks details on return values, error cases, or operational constraints, making it inadequate for full agent understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description adds no parameter semantics beyond implying a 'projectId' is needed. It doesn't explain what 'projectId' refers to (e.g., numeric ID, name) or format requirements, so it doesn't compensate for the low coverage, resulting in a baseline score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List all fields') and resource ('in a GitHub project'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'get_field_value' or 'update_project_field', which prevents a score of 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'get_field_value' or 'create_project_field'. It lacks context about prerequisites, such as needing an existing project, or exclusions, leaving the agent with minimal usage direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it's a list operation, implying read-only behavior, but doesn't cover critical aspects like pagination (implied by 'limit' parameter), error handling, authentication needs, rate limits, or what the output looks like. This is inadequate for a tool with parameters and no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with zero wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place by conveying essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 2 parameters (with 0% schema coverage), no annotations, no output schema, and multiple sibling list tools, the description is incomplete. It doesn't explain parameter semantics, behavioral traits, or differentiate from alternatives, leaving significant gaps for the agent to operate effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, so the description must compensate. It mentions 'GitHub project' which hints at 'projectId', but doesn't explain what 'projectId' is (e.g., a numeric ID or URL) or how 'limit' works (e.g., max items, default value). The description adds minimal value beyond the schema's structure.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'list' and the resource 'items in a GitHub project', making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list_issues' or 'list_projects', which would require specifying what type of items are being listed (e.g., issues, pull requests, notes).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'list_issues' or 'list_projects'. There's no mention of prerequisites, context, or exclusions, leaving the agent to infer usage based on the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'List GitHub projects' implies a read-only operation, but it doesn't specify whether this requires authentication, how results are returned (e.g., pagination, format), or any rate limits. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise at three words, with no wasted language. It's front-loaded with the core action and resource, making it efficient for quick understanding, though this brevity contributes to gaps in other dimensions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (a listing operation with 2 parameters, 1 required), no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't explain what the tool returns, how parameters affect results, or behavioral constraints, leaving the agent with insufficient context to use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, meaning neither parameter ('status' and 'limit') is documented in the schema. The description provides no information about these parameters—what 'status' values are valid, what 'limit' controls, or their expected formats. With two undocumented parameters, the description fails to compensate for the schema's lack of documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('List') and resource ('GitHub projects'), making the purpose immediately understandable. However, it doesn't distinguish this from sibling tools like 'get_project' (singular) or 'list_project_items', which might list items within projects rather than projects themselves.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'get_project' (singular retrieval) and 'list_project_items' (listing items within projects), there's no indication of when this list operation is preferred or what distinguishes it from other listing tools in the context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('List all views') but fails to describe key behaviors like whether this is a read-only operation, how results are formatted (e.g., pagination, sorting), or any rate limits. This leaves significant gaps in understanding the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized for a simple listing tool, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is incomplete. It lacks details on behavioral traits, parameter usage, and output format, which are essential for an agent to invoke the tool correctly. This makes it inadequate for practical use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 1 parameter with 0% description coverage, so the description must compensate. It mentions 'in a GitHub project', which implies the 'projectId' parameter is required, but does not explain what format the ID should be (e.g., numeric, string), where to find it, or any constraints. This adds minimal value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('List') and resource ('views in a GitHub project'), making the purpose specific and understandable. However, it does not distinguish this tool from potential siblings like 'list_project_fields' or 'list_project_items', which also list project-related resources, leaving some ambiguity in differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, such as 'list_project_items' or 'get_project', nor does it mention prerequisites like needing a valid project ID. Without such context, the agent lacks clear usage instructions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. 'List all sprints' implies a read-only operation, but it doesn't specify if it requires authentication, how results are returned (e.g., pagination, sorting), or any rate limits. This leaves significant gaps in understanding the tool's behavior beyond the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with just three words, front-loading the core action and resource. There is no wasted language, making it efficient and easy to parse, though this brevity contributes to gaps in other dimensions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (a list operation with 1 required parameter), no annotations, no output schema, and low schema coverage (0%), the description is incomplete. It doesn't explain the parameter, return format, or behavioral aspects, making it inadequate for effective tool selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 1 required parameter ('status') with 0% description coverage, meaning the parameter is undocumented in the schema. The description 'List all sprints' doesn't mention any parameters or explain what 'status' means (e.g., possible values like 'active', 'completed'), failing to compensate for the low schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'List all sprints' clearly states the verb ('List') and resource ('sprints'), making the basic purpose understandable. However, it doesn't specify what constitutes 'all' (e.g., active, completed, all statuses) or differentiate from sibling tools like 'get_current_sprint' or 'get_sprint_metrics', leaving it vague about scope and distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_current_sprint' for current sprint data or 'get_sprint_metrics' for performance insights, nor does it indicate any prerequisites or exclusions for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but lacks critical behavioral details. It mentions 'AI-powered analysis' but doesn't disclose processing time, rate limits, authentication needs, or what happens to the input PRD. For a complex tool with 18 parameters, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose. Every word contributes to understanding what the tool does, though it could be slightly more structured by separating purpose from the comparison note.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the high complexity (18 parameters, 14 required), no annotations, no output schema, and 0% schema coverage, the description is inadequate. It explains the purpose well but omits essential details about parameters, behavior, and output format, leaving the agent with insufficient context for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, meaning none of the 18 parameters are documented in the schema. The description provides no information about any parameters—not even the required 'prdContent' or what 'maxTasks' controls. This fails to compensate for the complete lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('parse') and resource ('Product Requirements Document'), specifies the output ('comprehensive list of actionable development tasks'), and distinguishes from siblings by mentioning 'AI-powered analysis' and 'similar to claude-task-master functionality'. This differentiates it from other tools like 'enhance_prd' or 'generate_prd'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives is provided. The description doesn't mention prerequisites, when-not scenarios, or compare it to sibling tools like 'enhance_prd' or 'analyze_task_complexity'. The agent must infer usage from the purpose alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden but only states the action without behavioral details. It doesn't disclose if this is a read-only or mutation operation, what permissions are needed, how it handles errors, or what the output looks like. For a tool that likely creates or modifies sprints, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words, making it easy to scan. It's appropriately sized for the tool's apparent complexity, though this conciseness comes at the cost of detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, 0% schema coverage, no output schema, and a likely mutation operation (planning/creating sprints), the description is incomplete. It lacks details on behavior, parameters, and outcomes, making it inadequate for safe and effective use by an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate but adds no parameter details. It mentions 'selected issues' which hints at 'issueIds', but doesn't explain the 'sprint' object's fields (title, dates, goals) or their formats. With 2 parameters and nested objects, this leaves critical semantics undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Plan a new sprint with selected issues' clearly states the action (plan) and resource (sprint), but it's vague about what 'plan' entails compared to sibling tools like 'create_sprint'. It doesn't specify whether this creates the sprint or just prepares it, leaving ambiguity in differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like 'create_sprint' or 'add_issues_to_sprint'. The description implies usage for new sprints with issues, but it doesn't clarify prerequisites, exclusions, or specific contexts, offering minimal direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Remove issues from a sprint' implies a destructive mutation, but it fails to specify critical details: whether this requires admin permissions, if it's reversible, what happens to the issues afterward (e.g., moved to backlog or deleted), or any rate limits. This leaves significant gaps for safe and effective tool invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with zero waste—'Remove issues from a sprint' is maximally concise. Every word earns its place by directly conveying the core action and target, making it easy to parse quickly without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (a destructive mutation with 2 parameters), lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It omits essential context: behavioral traits (e.g., side effects, permissions), parameter details, and expected outcomes. For a mutation tool, this inadequacy increases the risk of misuse by an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, with two required parameters ('sprintId' and 'issueIds') undocumented. The description adds no meaning beyond what the schema provides—it doesn't explain what these IDs represent, their format, or constraints (e.g., issueIds must be from the same project). With low schema coverage, the description fails to compensate, leaving parameters semantically unclear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Remove issues from a sprint' clearly states the verb ('Remove') and resource ('issues from a sprint'), making the purpose immediately understandable. It distinguishes from siblings like 'remove_project_item' by specifying the target resource as 'issues' and context as 'sprint'. However, it lacks specificity about what 'remove' entails operationally (e.g., unassigning vs. deleting).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., issues must be in the sprint first), exclusions, or related tools like 'add_issues_to_sprint' or 'update_issue' for reassignment. Without such context, the agent must infer usage from the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Remove' implies a destructive mutation, it doesn't specify whether this is reversible, what permissions are required, what happens to associated data, or what the response looks like. For a destructive operation with zero annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that states the core action without unnecessary words. It's appropriately sized for a simple operation and front-loads the essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a destructive mutation tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain parameter semantics, behavioral implications, or usage context, leaving significant gaps for an agent to understand and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema provides no parameter documentation. The description mentions 'an item from a GitHub project' but doesn't explain what 'projectId' and 'itemId' represent, their expected formats, or where to obtain them. It adds minimal value beyond the parameter names themselves.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Remove') and resource ('an item from a GitHub project'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'remove_issues_from_sprint' or 'delete_project', which have similar destructive semantics but target different resources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided about when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing project/item IDs from list operations), exclusions, or comparisons to similar tools like 'delete_project' or 'remove_issues_from_sprint'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions that it 'Supports all field types', it doesn't address critical behavioral aspects like required permissions, whether this is a mutation operation, error handling, or what happens when setting invalid values for specific field types.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately concise with two clear sentences. The first states the core purpose, and the second adds important context about supported field types without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with 4 parameters, 0% schema coverage, no annotations, and no output schema, the description is insufficient. It doesn't explain parameter meanings, behavioral implications, or what to expect from the operation, leaving significant gaps for an agent to understand how to use this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage for all 4 parameters, the description provides no information about what 'projectId', 'itemId', 'fieldId', or 'value' represent. The mention of field types gives some context for the 'value' parameter, but doesn't explain format requirements or how to reference the other IDs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Set a field value') and the target resource ('for a GitHub project item'), which provides a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'update_project_field' or 'get_field_value', which would be needed for a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, appropriate contexts, or exclusions, and doesn't reference any sibling tools for comparison or specific use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. 'Update' implies mutation, but the description doesn't mention permission requirements, whether changes are reversible, rate limits, or what happens to fields not specified. This leaves significant gaps for a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise at just three words, making it easy to parse. It's front-loaded with the essential action and resource, with no wasted words or unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with 7 parameters, 0% schema coverage, no annotations, and no output schema, the description is severely incomplete. It doesn't address behavioral aspects, parameter meanings, or usage context, leaving the agent with insufficient information to use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage for 7 parameters, the description provides no information about what the parameters mean or how to use them. It doesn't explain what 'issueId' refers to, what format 'assignees' expects, or what the 'status' enum represents. The description fails to compensate for the schema's lack of documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Update a GitHub issue' clearly states the verb ('Update') and resource ('GitHub issue'), making the purpose immediately understandable. However, it doesn't differentiate this tool from similar sibling tools like 'update_milestone' or 'update_project' beyond the resource type, which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'create_issue' for creation and 'get_issue' for reading, there's no indication of prerequisites, appropriate contexts, or exclusions for this update operation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. 'Update' implies a mutation operation, but it doesn't specify whether this requires specific permissions, if changes are reversible, what happens to unspecified fields, or what the response looks like. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It's appropriately sized and front-loaded, stating the core purpose immediately without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (5 parameters, mutation operation), lack of annotations, and no output schema, the description is incomplete. It doesn't provide enough context for an agent to understand how to use the tool effectively, what to expect in return, or the implications of using it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, meaning none of the 5 parameters are documented in the schema. The description adds no information about parameters beyond the tool name, failing to compensate for the coverage gap. It doesn't explain what 'milestoneId', 'title', 'description', 'dueDate', or 'state' represent or how to use them.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Update' and resource 'GitHub milestone', making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling 'update_issue' or 'update_project' tools, which also update GitHub resources, nor does it mention what aspects of a milestone can be updated beyond the generic term.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing milestone ID), when not to use it (e.g., for creating milestones, which has a sibling 'create_milestone'), or how it compares to other update tools like 'update_issue' or 'update_project'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'update' which implies a mutation, but doesn't specify permissions required, whether changes are reversible, rate limits, or what happens to unspecified fields. This leaves significant gaps for a tool with mutation behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to scan and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a mutation tool with 5 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't cover parameter meanings, behavioral traits, or usage context, leaving the agent with insufficient information to use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must compensate for all parameters. It mentions no parameters at all, failing to explain what fields can be updated (e.g., title, description, visibility, status) or their meanings. This leaves the 5 parameters undocumented beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'update' and the resource 'existing GitHub project', making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'update_issue' or 'update_milestone' beyond the resource type, which keeps it from a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'create_project' or other update tools. It lacks any mention of prerequisites, such as needing an existing project ID, or context for when updates are appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Update' which implies a mutation operation, but doesn't mention permissions required, whether changes are reversible, rate limits, or what the response looks like. This is a significant gap for a mutation tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that gets straight to the point with no wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with 6 parameters, 0% schema description coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain parameter usage, behavioral implications, or return values, leaving too many gaps for effective tool selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 6 parameters with 0% description coverage, so the description must compensate. It only mentions 'a custom field' without explaining what parameters like 'projectId', 'fieldId', 'options', or 'required' mean or how they should be used. This leaves most parameters undocumented and unclear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Update') and resource ('a custom field in a GitHub project'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'update_project' or 'set_field_value', which might handle similar resources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'create_project_field' or 'set_field_value'. It lacks context about prerequisites, such as needing an existing project and field, or exclusions for when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is an update operation, implying mutation, but doesn't cover critical aspects like required permissions, whether changes are reversible, rate limits, or what the response looks like. For a mutation tool with zero annotation coverage, this is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that gets straight to the point with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (a mutation with 4 parameters, no annotations, and no output schema), the description is inadequate. It doesn't explain parameter meanings, behavioral traits, or usage context, leaving significant gaps for an agent to understand and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, so parameters are undocumented in the schema. The description adds no information about what 'projectId', 'viewId', 'name', or 'layout' mean, their formats, or how they interact. It doesn't even mention that 'layout' has enum values, leaving the agent to rely solely on the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Update') and resource ('a view in a GitHub project'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'update_project' or 'update_project_field', which also modify GitHub project elements, leaving some ambiguity about scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing view), exclusions, or how it differs from related tools like 'create_project_view' or 'update_project', leaving the agent to infer usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Update' implies a mutation, but doesn't specify required permissions, whether changes are reversible, error handling, or response format. For a mutation tool with 6 parameters, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words. It's appropriately sized for a basic purpose statement, though its brevity contributes to gaps in other dimensions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (mutation tool with 6 parameters), lack of annotations, 0% schema description coverage, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how parameters interact, or behavioral aspects, making it inadequate for reliable agent use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema provides no parameter documentation. The description doesn't mention any parameters, leaving all 6 parameters (sprintId, title, description, startDate, endDate, status) undocumented. This fails to compensate for the schema's lack of descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Update a development sprint' clearly states the verb ('Update') and resource ('development sprint'), making the purpose understandable. However, it lacks specificity about what aspects can be updated and doesn't distinguish it from sibling tools like 'update_issue' or 'update_milestone', which have similar update operations on different resources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing sprint), exclusions, or compare it to related tools like 'create_sprint' or 'plan_sprint'. This leaves the agent without context for appropriate tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.