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. While 'Upload a file attachment' implies a write/mutation operation, it lacks critical details: required permissions (e.g., edit access to the case), file size/type limits, whether the attachment is public or private, or how failures are handled. This is inadequate 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 front-loads the core action and target. There's no wasted wording or redundancy, making it easy to parse quickly while conveying the essential 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 the complexity of a file upload operation (mutation with potential side effects), no annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like error handling, response format, or constraints, leaving significant gaps for the agent to navigate blindly.

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 100%, with clear parameter descriptions in the schema itself (e.g., 'TestRail case ID', 'Path to the file to upload as attachment'). The description adds no additional semantic context beyond what's already in the schema, such as example file paths or case ID formats. This meets the baseline for high schema coverage but doesn't enhance 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 ('Upload a file attachment') and target resource ('to a TestRail test case'), making the purpose immediately understandable. However, it doesn't differentiate from potential sibling tools like 'add_case' or 'add_result' that also involve adding content to TestRail, leaving room for confusion about when to choose this specific tool.

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 'add_case' (for creating cases) and 'add_result' (for adding test results), there's no indication of whether this is for supplementary files, required documentation, or specific contexts like bug reports. 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 full burden. It clearly indicates this is a creation/mutation operation ('Create a new TestRail test case'), mentions important prerequisites (gathering data from other tools), and hints at complexity (custom fields may be required by project configuration). However, it doesn't explicitly mention authentication needs, rate limits, or what happens on failure.

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 sized (3 sentences) and front-loaded with the core purpose. Every sentence adds value: first states what the tool does, second provides crucial prerequisite guidance, third offers alternative approach. Could be slightly more concise by combining the last two sentences.

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 creation tool with 6 parameters (including nested objects) and no annotations/output schema, the description does well by emphasizing prerequisites and complexity. However, it doesn't describe what the tool returns (no output schema exists) or potential error conditions, leaving some gaps for a mutation 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 100%, so the schema already documents all 6 parameters thoroughly. The description doesn't add significant parameter semantics beyond what's in the schema - it references the same tools (get_sections, get_case_fields) that the schema descriptions mention. Baseline 3 is appropriate when schema does the heavy lifting.

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 explicitly states 'Create a new TestRail test case in a specific section' - a clear verb ('Create') + resource ('TestRail test case') + location constraint ('in a specific section'). This distinguishes it from sibling tools like 'update_case' (modification) and 'get_case' (retrieval).

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 explicit guidance on when to use this tool: 'Before creating a case, gather required information using get_projects, get_suites, get_sections, and get_case_fields tools' and 'Or ask the user to provide the information if not provided.' It names specific prerequisite tools and offers an alternative approach when data is missing.

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. While 'adds' implies a write operation, it doesn't specify permissions required, whether it's idempotent, error handling, or side effects (e.g., notifications). For a mutation tool with 9 parameters, this lack of behavioral context 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 front-loads the core functionality without unnecessary words. Every part ('adds a new test result, comment, or assigns a test') directly contributes to understanding the tool's purpose, making it well-structured and concise.

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 9 parameters, no annotations, and no output schema, the description is incomplete. It lacks behavioral details (e.g., permissions, side effects), usage guidelines relative to siblings, and any information about return values or errors, leaving the agent with insufficient context for reliable 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 100%, so the schema already documents all parameters thoroughly. The description mentions 'test result, comment, or assigns a test', which loosely maps to parameters like status_id, comment, and assignedto_id, but adds minimal semantic value beyond what's in the schema. Baseline 3 is appropriate when schema does the heavy lifting.

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 ('adds', 'assigns') and resources ('test result', 'comment', 'test'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'update_test' or 'add_case', which could also modify test-related data.

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 'update_test' and 'add_case' available, there's no indication of whether this is for initial results, updates, or specific scenarios, leaving the agent to guess based on 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 'fetch', implying a read operation, but lacks details on permissions, rate limits, error handling, or response format. For a tool with no annotations, this leaves significant gaps in understanding its behavior and constraints.

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 any unnecessary words. It is front-loaded and appropriately sized, making it easy to parse quickly while conveying the essential action.

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 and output schema, the description is incomplete. It doesn't explain what data is returned (e.g., case details, fields), potential errors, or how it differs from sibling tools. For a retrieval tool in a context with multiple similar tools, 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?

The description mentions 'by ID', which aligns with the single parameter 'case_id' in the schema. With 100% schema description coverage, the schema already documents the parameter as 'TestRail case ID' with type and constraints, so the description adds minimal value beyond reinforcing the ID-based lookup, meeting the baseline for high 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 clearly states the verb 'fetch' and the resource 'TestRail test case by ID', making the purpose specific and understandable. However, it doesn't explicitly distinguish this tool from similar siblings like 'get_cases' (plural) or 'get_test', which also retrieve test-related data, leaving some ambiguity about when to use this specific tool versus others.

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_cases', 'get_test', and 'get_case_fields', there is no indication of context, prerequisites, or exclusions, such as whether this is for single-case retrieval versus bulk operations or other related queries.

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 it 'Returns a list,' implying a read-only operation, but doesn't specify if it requires authentication, has rate limits, returns paginated results, or what format the list takes (e.g., JSON array of field objects). For a tool with zero annotation coverage, this is insufficient 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 ('Returns a list...') with no wasted words. Every part of the sentence earns its place by specifying what is returned and for what resource, making it optimally 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 tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks context on usage, behavior, or output format. For a read operation with no complex inputs, this is acceptable but leaves gaps that could hinder an agent's effective use, especially without annotations to fill in behavioral details.

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 parameters with 100% coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter details, and it correctly implies no parameters are required by not mentioning any. This meets the baseline for zero-parameter tools, though it doesn't explicitly state 'no parameters needed,' which would have warranted a 5.

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 ('Returns') and resource ('list of available test case custom fields'), making the purpose immediately understandable. It distinguishes this from siblings like get_case or get_cases by specifying it returns custom fields rather than cases themselves. However, it doesn't explicitly contrast with all siblings, so it falls short of a perfect 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. It doesn't mention prerequisites (e.g., needing a project context), when it's appropriate (e.g., before creating cases with custom fields), or what siblings might be better for related tasks (like get_case for case details). This leaves the agent without contextual 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. It mentions 'optional filtering and pagination,' which hints at behavior, but doesn't disclose critical details like whether this is a read-only operation, potential rate limits, authentication needs, or what the return format looks like (e.g., list structure, error handling). For a tool with 18 parameters and no 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, well-structured sentence that efficiently conveys the core purpose and key features (filtering, pagination) without unnecessary words. It's front-loaded and every part 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 (18 parameters, no annotations, no output schema), the description is incomplete. It covers the basic purpose but lacks behavioral details (e.g., read-only nature, response format) and deeper usage guidance. The high schema coverage helps, but for a tool with many parameters and no output schema, more context would be beneficial.

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 100%, so the schema already documents all 18 parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'optional filtering and pagination,' which loosely maps to some parameters like 'limit' and 'offset,' but doesn't provide additional syntax, format, or usage details. Baseline 3 is appropriate when the schema does the heavy lifting.

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 a list of test cases for a project or specific test suite with optional filtering and pagination.' It specifies the verb ('Get'), resource ('test cases'), and scope ('project or specific test suite'), but doesn't explicitly distinguish it from sibling tools like 'get_case' (singular) or 'get_sections'.

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 mentioning 'project or specific test suite' and 'optional filtering and pagination,' but doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_case' (for a single case) or 'get_sections' (for sections). No exclusions or prerequisites are 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 'Get details' but doesn't clarify if this is a read-only operation, what permissions are required, or what the response format includes (e.g., project name, settings). This leaves significant gaps for a tool that likely interacts with a database or API.

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 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 low complexity (single parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavior, usage context, and output, which are needed for full understanding in the absence of annotations.

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 100% description coverage, with the parameter 'project_id' well-documented as a positive integer. The description adds minimal value beyond the schema by specifying 'by ID', but it doesn't explain format constraints or provide examples, so it meets the baseline for high 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 clearly states the verb ('Get details') and resource ('specific TestRail project by ID'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_projects' (plural) or 'get_case', which also retrieve details but for 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 sibling tools like 'get_projects' (for listing projects) or specify prerequisites such as needing a valid project ID, leaving usage context implied rather than explicit.

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 whether this is paginated, requires authentication, has rate limits, returns structured data, or what happens with large project sets—critical 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—front-loaded and to the point. Every word contributes directly to stating the tool's purpose without redundancy or fluff.

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, no output schema, and a simple list operation, the description is incomplete. It lacks details on return format, pagination, error handling, or how it fits with siblings like 'get_project', leaving gaps for an agent to use it effectively in 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?

The tool has 0 parameters with 100% schema coverage, so no parameter documentation is needed. The description doesn't add parameter info, which is appropriate, but it also doesn't imply any hidden parameters or constraints, keeping it straightforward.

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 ('TestRail projects'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_project' (singular) or explain scope beyond 'all', missing full sibling 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?

No guidance is provided on when to use this tool versus alternatives like 'get_project' (singular) or 'get_cases' (which might be project-specific). The description implies a broad listing but offers no context about prerequisites, filtering, or comparison to 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?

No annotations are provided, so the description carries the full burden. It states this is a read operation ('Returns'), which is clear, but doesn't disclose behavioral traits such as error handling (e.g., what happens if the run_id is invalid), authentication needs, rate limits, or response format. The reference to 'get tests' adds some context but is insufficient for a mutation-free tool with no 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 two sentences, front-loaded with the core purpose and efficiently references another tool for additional context. Every sentence earns its place with no wasted words, making it appropriately sized and easy to parse.

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 simplicity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and points to related information, but as a read tool with no annotations, it should ideally include more on response behavior or error cases to be fully complete for 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 100%, with the single parameter 'run_id' well-documented in the schema as 'The ID of the test run'. The description doesn't add any meaning beyond this, such as format examples or constraints not in the schema. Baseline 3 is appropriate since the schema does the heavy lifting.

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 ('Returns') and resource ('an existing test run'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_runs' (plural) or 'get_test', which could retrieve similar resources. The mention of 'get tests' is helpful but not a direct sibling 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 implies usage by referencing 'get tests' for related information, suggesting it's for retrieving a specific run's details. However, it lacks explicit guidance on when to use this versus alternatives like 'get_runs' (for multiple runs) or 'get_test' (for individual tests), and doesn't mention 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. It mentions 'optional filtering and pagination,' which hints at functionality, but lacks details on permissions, rate limits, error handling, or the return format (e.g., structure of the list). For a tool with 10 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 ('Get a list of test runs for a project') and adds key constraints ('with optional filtering and pagination. Only returns test runs that are not part of a test plan.'). Every part earns its place without redundancy or waste.

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 adequate but incomplete. It covers the basic purpose and a key constraint (excludes test plan runs), but lacks details on behavioral aspects like response format, error cases, or usage nuances. This makes it minimally viable but with clear gaps for effective tool 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 100%, meaning all parameters are documented in the input schema. The description adds minimal value beyond the schema by mentioning 'optional filtering and pagination,' which aligns with parameters like created_after, limit, and offset, but doesn't provide additional syntax or usage details. This meets the baseline of 3 when schema coverage is high.

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 a list of') and resource ('test runs for a project'), making the purpose specific and understandable. It distinguishes this tool from 'get_run' (singular) by indicating it returns multiple runs, but doesn't explicitly differentiate from other list tools like 'get_cases' or 'get_tests' 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 clear context by specifying 'Only returns test runs that are not part of a test plan,' which helps guide when to use this tool. However, it doesn't explicitly mention when to use alternatives like 'get_run' (for a single run) or other filtering tools, nor does it state any prerequisites beyond the required project_id.

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 'optional pagination' which hints at list behavior, but doesn't describe return format, error conditions, rate limits, authentication needs, or what happens with invalid inputs. For a read operation with 4 parameters, this lacks critical operational context.

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 action, scope, and key feature. It's front-loaded with the main purpose and avoids redundancy or unnecessary details. Every word earns its place, making it highly concise and readable.

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 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on return values, error handling, and practical usage scenarios. For a tool that fetches structured data, more context is needed to help an agent use it effectively, especially without annotations to fill gaps.

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 100%, so the schema fully documents all 4 parameters. The description adds minimal value beyond the schema, mentioning 'optional pagination' which loosely relates to 'limit' and 'offset', but doesn't clarify semantics like the relationship between project_id and suite_id or default behaviors. Baseline 3 is appropriate as the schema does the heavy lifting.

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 a list of') and resource ('sections'), specifying the scope ('for a project and test suite') and optional feature ('with optional pagination'). It distinguishes from siblings like 'get_cases' or 'get_suites' by focusing on sections, but doesn't explicitly contrast with them. The purpose is specific and actionable, though not maximally differentiated.

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 mentions the context ('for a project and test suite') but doesn't specify use cases, prerequisites, or exclusions. With siblings like 'get_cases' or 'get_suites' available, there's no indication of when sections are needed over those resources, leaving usage ambiguous.

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 details' but doesn't specify what details are returned (e.g., suite name, description, project association), whether it's a read-only operation, or any error handling (e.g., for invalid IDs). 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, clear sentence that efficiently conveys the core purpose without unnecessary words. It's front-loaded and wastes no space, making it easy for an agent 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'details' are returned, which is critical for a tool with no structured output documentation. For a simple read operation, more context on the response format would help the agent 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?

The description mentions 'by ID', which aligns with the single parameter 'suite_id' in the input schema. Since schema description coverage is 100%, the schema already documents the parameter as 'TestRail suite ID' with type and constraints. The description adds minimal value beyond this, meeting the baseline for high 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 clearly states the action ('Get details') and resource ('TestRail test suite by ID'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_suites' (plural) or 'get_case' which suggests this is for a single suite, but this distinction isn't explicitly stated.

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_suites' (for listing multiple suites) or other 'get_' tools for different resources. It lacks context about prerequisites, such as needing a valid suite ID, or exclusions, leaving the agent to 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?

No annotations are provided, so the description carries the full burden. It states the action ('Get all test suites') but lacks behavioral details such as whether this is a read-only operation, if it requires authentication, any rate limits, pagination behavior, or error handling. For a tool with zero annotation coverage, this is a significant gap in disclosing operational traits.

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 ('Get all test suites') and specifies the context ('for a specific TestRail project by ID'). There is zero waste, making it highly concise and well-structured for quick 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's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and parameter context, but lacks completeness in behavioral aspects like safety or output details, which are important for a tool with no annotations or output schema to guide 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?

The schema description coverage is 100%, with the single parameter 'project_id' documented as 'TestRail project ID'. The description adds no additional meaning beyond this, such as format examples or constraints not in the schema. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't detract either.

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 ('all test suites'), specifying the scope ('for a specific TestRail project by ID'). It distinguishes from siblings like 'get_suite' (singular) by indicating it retrieves multiple suites, though it doesn't explicitly contrast with other list tools like 'get_cases' or 'get_projects' 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 implies usage when needing suites for a project, but provides no explicit guidance on when to use this versus alternatives like 'get_suite' (for a single suite) or other list tools. It mentions the required 'project_id' parameter, which hints at prerequisites, but lacks context on exclusions or comparisons 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns data (a read operation), but doesn't cover aspects like authentication needs, rate limits, error handling, or what the return format looks like (e.g., JSON structure). For a read 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 extremely concise—a single sentence with zero waste. It's front-loaded with the core action ('Returns'), making it easy to scan. Every word earns its place, though this brevity contributes to gaps in other dimensions like guidelines and transparency.

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 read operation with 2 parameters) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'test' data is returned, how to interpret 'with_data,' or any behavioral traits. For a tool with no structured output and full parameter reliance on the schema, more context is needed to be fully 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 description adds no meaning beyond what the input schema provides. Schema description coverage is 100%, with clear documentation for 'test_id' (ID of the test) and 'with_data' (parameter to get data). Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate or add extra context.

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 'Returns an existing test' states the basic action (return) and resource (test), but it's vague—it doesn't specify what a 'test' entails in TestRail (e.g., test case details, results) or differentiate it from sibling tools like 'get_tests' (plural). It avoids tautology by not just restating the name, but lacks specificity.

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. For example, it doesn't explain if this is for retrieving a single test by ID (vs. 'get_tests' for multiple tests) or mention prerequisites like needing a valid test ID. The description implies usage only through the action 'returns,' but no explicit context or exclusions are 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 full burden for behavioral disclosure but offers minimal information. It doesn't mention authentication requirements, rate limits, pagination behavior beyond parameters, response format, error conditions, or whether this is a read-only operation (though 'Returns' implies it). For a tool with 5 parameters and no 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, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a list-retrieval tool and front-loads the essential information. 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 tool with 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't address key contextual aspects like authentication needs, rate limits, pagination behavior, response format, or error handling. The agent would need to guess about important behavioral characteristics when invoking this tool.

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 100%, so the schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain relationships between parameters (like how status_id and label_id interact) or provide examples. This meets the baseline for high 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 clearly states the action ('Returns a list') and resource ('tests for a test run'), making the purpose immediately understandable. It distinguishes from sibling tools like 'get_test' (singular) by specifying it returns multiple tests, but doesn't explicitly differentiate from other list tools like 'get_cases' or 'get_runs' 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. It doesn't mention when filtering by status or label is appropriate, when pagination is needed, or how this differs from similar tools like 'get_cases' which might return related data. The agent must infer usage from parameter 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?

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 mention permissions required, whether changes are reversible, rate limits, error handling, or what the response looks like (since there's no output schema). 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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part ('Update a TestRail test case by ID with new field values') contributes directly to understanding the tool, making it appropriately sized and well-structured for quick comprehension.

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 (7 parameters, mutation operation, no annotations, no output schema), the description is insufficient. It lacks details on behavioral traits (e.g., side effects, authentication), doesn't explain return values or errors, and provides minimal context for usage. For a mutation tool with rich parameters, this leaves the agent under-informed about critical aspects.

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 100%, so the schema already documents all 7 parameters thoroughly. The description adds no additional meaning beyond implying that parameters represent 'new field values,' which is redundant with the schema. This meets the baseline of 3 for high schema coverage, but doesn't compensate with extra insights like field interdependencies or examples.

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'), resource ('TestRail test case'), and mechanism ('by ID with new field values'), which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'update_run' or 'update_test', which also update TestRail entities, leaving some ambiguity about when to choose this specific tool.

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_case' (for creation) or other update tools (e.g., 'update_run'). It mentions updating by ID but doesn't specify prerequisites (e.g., needing an existing case ID) or exclusions (e.g., not for bulk updates), leaving usage context implied rather than explicit.

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 mentions partial updates are supported (useful context), it doesn't address critical aspects like required permissions, whether changes are reversible, rate limits, or what happens to unspecified fields during partial updates. For a mutation tool with 12 parameters, this leaves significant 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 with just two sentences that both earn their place. The first sentence states the core purpose, and the second adds important behavioral context about partial updates. No wasted words or redundant 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 mutation tool with 12 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, error conditions, authentication requirements, or how it differs from similar update tools. The mention of partial updates is helpful but doesn't compensate for the broader gaps.

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 mentions 'partial updates are supported' which provides context about how parameters work together, but doesn't add specific meaning to individual parameters beyond what's already in the schema (which has 100% coverage). The baseline of 3 is appropriate since the schema does the heavy lifting of documenting all parameters.

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 ('Updates') and resource ('an existing test run'), and specifies that partial updates are supported. However, it doesn't differentiate this tool from sibling tools like 'update_case' or 'update_test' 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 like 'update_case' or 'update_test'. It mentions partial updates are supported, but doesn't explain when this might be preferred over creating a new run or using other update tools.

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 this is an update operation (implying mutation) but doesn't cover critical aspects like required permissions, whether changes are reversible, rate limits, or what happens to existing labels not mentioned. 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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every word earning 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 no annotations and no output schema, the description is insufficient. It doesn't explain what the update operation returns, error conditions, or behavioral constraints. Given the complexity (3 parameters including nested objects) and lack of structured coverage, more context is needed 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?

Schema description coverage is 100%, so the schema fully documents all three parameters. The description mentions 'labels' but doesn't add meaningful semantic context beyond what the schema provides (e.g., explaining label types or custom field usage). Baseline 3 is appropriate when the schema does the heavy lifting.

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 ('Updates') and resource ('labels assigned to an existing test'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'update_case' or 'update_run', which likely update different aspects of TestRail entities.

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 test), exclusions, or comparisons to sibling tools like 'update_case' or 'update_run', 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.