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

No annotations provided, so description carries the burden. It explains parent parameter accepts ID or title and references default project for project_id. However, it does not disclose side effects, error handling, or idempotency, 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?

Concise with a clear structure: single-line purpose, a practical tip, required/optional breakdown, and three illustrative examples. No unnecessary words, and every sentence adds value.

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 output schema, and sibling tools like get_project_context, the description provides all necessary context: parameter roles, example payloads, and a helpful reference. Complete for its complexity level.

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 coverage is 100%, so baseline is 3. Description adds value by providing usage context for project_id (optional if default set), examples demonstrating title and parent as string or number, and a tip to resolve parent titles, going beyond 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 'Create a new test suite in TestCollab' with specific verb and resource. Examples and parameter lists reinforce purpose, distinguishing it from sibling tools like update_suite, delete_suite, and move_suite.

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?

Includes a tip to call get_project_context first to resolve parent suite names. Lists required and optional parameters, with examples for root and child suites. Does not explicitly exclude scenarios or mention alternatives, but context is sufficient for typical use.

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 the full burden. It correctly indicates a creation operation (write) but does not disclose any side effects, permissions needed, or the response format. The description is adequate but could be more transparent about what happens after creation.

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 fairly concise given the complexity (10 parameters). It front-loads the purpose, then groups required/optional, and provides structured examples. Minor redundancy exists but overall efficient.

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 (10 params, no output schema), the description covers parameters, formats, and a cross-tool tip. It lacks what the response will be (e.g., created test case ID), but still provides sufficient 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?

Input schema coverage is 100%, so baseline is 3. The description adds significant value by providing step and custom field formats, an example usage, and clarifying that suite can be an ID or title. This enriches the semantic understanding 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 explicitly states 'Create a new test case in TestCollab.' This is a specific verb-resource pair that clearly distinguishes it from sibling tools like create_suite or create_test_plan.

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 includes a helpful tip to call get_project_context first to resolve names to IDs, providing clear usage guidance. It also lists required and optional parameters. However, it does not explicitly contrast with alternatives like update_test_case or state 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?

With no annotations, the description carries full burden. It details the execution flow (4 POST steps), optional parameters, defaults (title), and behavior for assignee and assignment. It could mention side effects like notifications but overall is transparent.

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 well-structured with sections (prerequisites, execution flow, parameter details, example). It's somewhat lengthy but each part adds value. Could be slightly leaner, but front-loaded and clear.

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 12 parameters, nested objects, no output schema, and complex behavior, the description covers prerequisites, flow, parameters with examples, and defaults. It lacks potential error conditions or permissions but is largely complete.

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 coverage is 100% but description adds value: clarifies assignee supports 'me'/names, relationship between test_cases and assignment, and provides example JSON. Enhances understanding beyond 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 'Create a test plan in TestCollab using a single MCP tool call.' It distinguishes from siblings (e.g., update_test_plan, delete_test_plan) by focusing on creation and detailing the multi-step execution flow.

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 prerequisites ('Ask follow-up questions for missing required information', 'Do not infer or auto-generate required values like project_id') and an example. It lacks explicit when-not-to-use guidance but the context of creation vs update is implicit.

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?

The description warns that deletion 'may affect child suites and test cases', which is a key behavioral trait. However, it does not disclose whether deletion is reversible, requires specific permissions, or what happens to the test cases. Since no annotations are provided, the description partially addresses behavior but lacks completeness.

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 concise: three sentences covering action, warning, and parameter summary. It is front-loaded with the primary purpose and uses clear, direct language without superfluous details.

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 (two parameters, no output schema), the description covers the essential: what it does, a critical warning about cascading effects, and parameter identification. It does not mention return values or error handling, which are minor omissions for a delete 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?

With 100% schema coverage, the baseline is 3. The description repeats parameter roles ('Required: id (suite ID)', 'Optional: project_id') without adding new meaning beyond the schema's type and description. No additional constraints or usage hints are provided, so it adds minimal value.

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 'Delete a test suite from TestCollab', using a specific verb and resource. It distinguishes itself from sibling tools like create_suite, update_suite, and get_suite, leaving no ambiguity about its function.

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 lacks guidance on when to use this tool versus alternatives such as update_suite or move_suite. The warning to 'Use with caution' is generic and does not specify prerequisites or scenarios where deletion is appropriate, leaving the agent without decision 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, the description carries full burden. It explicitly warns about permanent deletion of the test plan and related execution data, which is critical behavioral information. However, it does not describe return values or error conditions.

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 plus a parameter line, with no wasted words. The purpose is front-loaded and the warning is prominently placed.

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 simplicity of the tool (2 parameters, no output schema), the description covers the key aspects: purpose, permanence warning, and parameter list. It could mention return behavior, but is otherwise complete.

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 baseline is 3. The description repeats the required/optional nature of the parameters without adding additional semantics 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 'Delete' and the resource 'a test plan from TestCollab'. It is specific and distinguishes from sibling tools like delete_suite.

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 deleting a test plan, but does not explicitly state when to use this tool versus alternatives or when not to use it. The warning about permanence provides context, but lacks 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?

The description details what is returned (project name, description, app_type, etc.) and explains that it helps resolve names to IDs. It also notes that app_type should inform test step generation. No annotations exist, so the description carries the burden, and it does well, though it does not explicitly state that the tool has no side effects.

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 informative but not excessively long. It uses a clear structure with a list of returned items and an important usage callout in bold. Every sentence serves a 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 tool's simplicity (one optional param, no output schema), the description is comprehensive. It covers the purpose, output, usage context, and even cues the agent on how to use the returned app_type. No gaps identified.

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 single parameter (project_id) has 100% schema coverage. The description does not add significant meaning beyond the schema's note about optionality. Baseline 3 is appropriate since the schema already documents the parameter 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 it retrieves project context including metadata for name-to-ID resolution. It lists specific items like suite tree, tags, custom fields, etc., and distinguishes itself from siblings by emphasizing its role as a prerequisite for other 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?

Explicitly instructs the agent to call this tool 'at the start of every conversation before using any other TestCollab tool' to avoid errors. This provides clear when-to-use guidance and implies exclusion of other tools for this purpose.

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?

The description indicates a read-only operation by stating 'Get details' and 'Returns...'. Since annotations are not provided, the description bears the full burden. It is clear that the tool does not mutate data, and it specifies the return fields. Although it does not explicitly state idempotency or lack of side effects, the wording strongly implies a safe fetch.

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 concise, consisting of three short sentences. The first sentence states the primary purpose, the second lists return fields, and the third specifies parameter usage. No extraneous information is present, and every sentence contributes value.

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 simple read tool with two parameters and no output schema or annotations, the description covers essential aspects: what it does, what it returns, and parameter roles. It lacks details about error handling or response structure beyond listed fields, but it is generally adequate for an agent to understand how to invoke 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 input schema already provides complete descriptions for both parameters (100% coverage). The description merely repeats the schema information (e.g., 'Required: id (suite ID) Optional: project_id'). It adds no additional meaning beyond what the schema offers, so the baseline score of 3 is appropriate.

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 details of a specific test suite by ID.' It also lists the specific fields returned (title, description, parent_id, and other metadata), which helps the agent understand exactly what data to expect. This differentiates it from sibling tools like create_suite, delete_suite, and list_suites.

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 identifies required and optional parameters: 'Required: id (suite ID) Optional: project_id'. However, it does not provide guidance on when to use this tool versus alternatives like list_suites (which lists all suites) or get_test_plan (which retrieves a different resource). There is no explicit 'when-to-use' or 'when-not-to-use' 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 provided; description indicates a read operation ('fetch') but lacks explicit mention of side effects, rate limits, or full return structure beyond mentioning 'steps and expected results'.

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?

Description is concise, front-loaded with the action and resource, includes example, and contains no unnecessary words.

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 output schema, the description provides some return context (steps, expected results) but could be more detailed about the full response structure. Still adequate for a simple retrieval 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 coverage is 100%, so baseline 3; the description adds an example and clarifies required/optional, but does not significantly enhance understanding 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 tool fetches a single test case with full details including steps and expected results, distinguishing it from list_test_cases which returns multiple cases.

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 specifies required and optional parameters with defaults, and provides an example, but does not explicitly indicate when to prefer this over sibling tools like list_test_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?

No annotations are provided, so the description bears full burden. It mentions the tool fetches data (read operation) and lists what is included, but does not disclose error handling, authentication requirements, rate limits, or what happens if the plan does not exist.

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 structured with a clear introductory sentence, bullet points listing included details, parameter guidance, and a code example. Every sentence earns its place with no redundancy or verbosity.

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 8 parameters (most with defaults), no output schema, and no annotations, the description covers core functionality and parameter usage well. It explains what the response includes (test cases count, configurations, runs, progress) but does not detail the response structure or error scenarios.

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 coverage is 100%, baseline 3. The description adds value by stating that 'id' accepts numeric ID or title string, providing explicit examples, and clarifying optional parameters like include_configurations and include_runs, which goes beyond the 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 'Fetch a single test plan with summary details' and enumerates specific included data. This distinguishes it from siblings like list_test_plans (which returns multiple) and get_test_case/get_suite (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 specifies required parameters (id or title) and optional ones, with examples showing usage. While it doesn't explicitly compare with alternatives, the context of sibling tools and the phrase 'single test plan' implies when to use this versus list_test_plans.

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?

Without annotations, the description partially covers behavioral traits by mentioning the hierarchical tree output and filter options. However, it does not disclose potential performance issues, pagination, authorization needs, or what happens if the project has many suites. The description adds some value but misses important behavioral 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 front-loaded with core purpose ('List all test suites in a TestCollab project as a hierarchical tree') and then details filters. It is efficient, though the filter list slightly duplicates schema info. Overall, every sentence contributes, but minor redundancy keeps it from a 5.

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?

The description covers output structure (id, title, parent_id, children) and lists filters, which is adequate for a list tool. However, no output schema exists, so the description should also mention default ordering, pagination, and additional fields (e.g., updated_at). This gap in completeness gives a score of 3.

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 coverage is 100%, so baseline is 3. The description adds value by explaining that title_contains is an alias for title and applied at the API level, and describes the output structure (node fields). This goes beyond simply repeating schema information.

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 lists all test suites in a project as a hierarchical tree with parent-child relationships. The verb 'list' and resource 'suites' are specific, and the hierarchical tree detail distinguishes it from a flat list. Sibling tools like get_suite (single) and list_test_cases (different resource) further clarify purpose.

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 for retrieving all suites in a tree format, but lacks explicit guidance on when to use this tool versus alternatives like get_suite. No exclusions or prerequisites are mentioned. The filters provide context for narrowing results but not when to choose this tool over others.

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 behavioral burden. It discloses that the tool may omit full step details, which is a key behavioral trait. However, it does not mention authentication, rate limits, or error handling, which would further enhance 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 well-structured and appropriately sized: a concise summary sentence, followed by tips, a structured list of filter fields, filter types, an example, and parameter defaults. Every sentence adds value, with no 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 (nested objects, no output schema), the description covers filtering, sorting, and pagination thoroughly, and includes usage tips. However, it lacks explicit description of the output format or list of returned fields, which would make it more complete.

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?

Despite 100% schema coverage, the description adds substantial value: it lists all filter fields with types, provides filter type categories (text, number, date), gives a concrete example filter, explains sort syntax, and specifies default values for limit and offset. This goes far 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 'List test cases from a TestCollab project with optional filtering, sorting, and pagination,' specifying the verb, resource, and scope. It also distinguishes from sibling tools by noting that list_test_cases may omit full step details, guiding users to use get_test_case for complete details.

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 explicitly advises calling get_project_context first to resolve IDs, and provides a note that list_test_cases may omit step details, recommending get_test_case for full details. This gives clear when-to-use and when-not-to-use guidance with specific 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?

With no annotations provided, the description carries the full burden. It lists filters and provides an example but does not disclose behavioral traits such as read-only nature, authorization requirements, rate limits, or error behavior. The example and filter details add some transparency, but there is room for improvement.

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 well-structured with a clear purpose sentence, a bulleted filter list, and a JSON example. While slightly verbose at around 150 words, every sentence contributes meaning. It could be a bit more concise without losing clarity.

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 23 parameters (all optional), no output schema, and no annotations, the description covers the filter parameters thoroughly and includes an example. However, it lacks explanation of the return format or behavior when no results are found. The tool is a list operation, so return structure is important context missing here.

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 coverage is 100%, so the baseline is 3. The description adds a brief listing of filters and an example, which provides some additional context but largely mirrors the schema descriptions. The value added is modest.

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', the resource 'test plans', and the context 'from a TestCollab project'. It distinguishes from siblings like list_suites and list_test_cases by being specific to test plans with filtering, sorting, and pagination.

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 explicitly mentions optional filtering, sorting, and pagination, and provides an example. However, it does not explicitly state when to use this tool versus alternatives like get_test_plan for a single plan or create_test_plan for creating. The usage context is clear but not exhaustive.

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?

The description lacks behavioral details beyond the move action. With no annotations, it should disclose effects on child suites, permissions needed, validation behavior, and whether the operation is reversible or idempotent.

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 concise: a one-line action, a tip, then parameter list with clear required/optional labels and two illustrative examples. Every sentence adds value with no 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 no annotations and no output schema, the description covers the essential information needed to call the tool correctly (params, examples, tip). It falls slightly short on behavioral transparency, but is otherwise complete.

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%, but the description adds value with examples showing that 'parent' can be a suite title (string) or null. This clarifies usage beyond the schema's type union.

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 'Move' and the resource 'test suite', with specific scope to 'a different parent suite, or to root level'. This distinguishes it from siblings like create_suite, delete_suite, and reorder_suites.

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 a practical usage tip ('Call get_project_context first') and explicitly lists required and optional parameters with examples. However, it does not explicitly state when not to use this tool or mention alternatives beyond the tip.

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 specifies the required parameters and gives examples, but it does not disclose any behavioral traits such as authorization requirements, side effects, or what happens if invalid IDs are provided. The description is clear enough for a straightforward set operation, but lacks deeper behavioral 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 very concise, consisting of a single-line purpose statement, a brief tip, a list of required/optional parameters, and two examples. Every sentence adds value, and there is no redundant or extraneous 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's low complexity (3 parameters, no nested objects) and no output schema, the description is largely complete. It covers the purpose, usage guidance, parameter semantics, and examples. However, it does not mention the return value (e.g., a success indicator), which would be beneficial for an operation that modifies data.

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 already provides descriptions for all three parameters (100% coverage). The description adds value by repeating the required parameters, clarifying that 'parent' can be null for root or a string title, and providing concrete examples that demonstrate how to pass the parameters. This aids interpretation beyond the schema alone.

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: 'Set the sort order of sibling suites under a given parent.' It uses a specific verb ('Set') and resource ('sort order of sibling suites'), distinguishing it from sibling tools like move_suite (which changes parent) and list_suites (which reads).

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 a helpful tip to call get_project_context or list_suites first to see current suite IDs, guiding when to use this tool. It also includes examples that clarify usage. However, it does not explicitly state when not to use the tool (e.g., if the goal is to move suites to a different parent, use move_suite instead).

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?

Discloses that only provided fields are updated (partial update behavior). Provides an example. However, without annotations, it does not mention any side effects, permissions, or error handling. Still, the key behavioral trait is covered.

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?

Three concise sentences plus an example. No wasted words. Important information is front-loaded.

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?

Covers input well but omits return value and error behavior. With no output schema and no annotations, the description could be more complete by stating what is returned (e.g., the updated suite object). Adequate but not outstanding.

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 coverage is 100%, so baseline is 3. The description adds the partial update concept and the move_suite distinction but does not significantly enhance parameter understanding beyond the schema's 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 it updates an existing test suite, with the specific verb 'update' and resource 'test suite'. It distinguishes from siblings like move_suite by explicitly directing the user to a different tool for moving suites.

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?

Provides explicit guidance on when to use this tool: to update a suite, and when not to: to move a suite, directing to move_suite. Lists required and optional parameters, including the note about project_id being optional if TC_DEFAULT_PROJECT is set.

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?

Without annotations, the description carries the full burden. It explains partial update semantics and the behavior of steps_patch (patch by step number without replacing all steps). Examples illustrate usage. However, it does not describe return values, error scenarios, or auth requirements, which would improve 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 fairly long but well-structured: purpose sentence, tips, field list, and examples. Every sentence adds value, but it could be slightly more compact. However, given the complexity, it is appropriately sized.

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 12 parameters, nested objects, and no output schema, the description is comprehensive. It covers all fields, includes usage examples, and provides integration tips with other tools. It is sufficient for an agent to correctly invoke the tool 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 coverage is 100%, but the description adds significant value: it organizes fields, clarifies priority values (0=Low, etc.), explains the difference between steps and steps_patch, and provides examples. This helps the agent select and fill parameters correctly.

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 ('test case'), and scope ('in TestCollab'). It also explicitly says only provided fields will be updated, which distinguishes it from full-replace operations. Sibling tools like create_test_case and get_test_case are different in purpose.

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 tips: call get_project_context to resolve IDs, and call get_test_case to get existing steps for patching. This guides the agent on prerequisites and alternatives. It also lists required and optional fields, making usage clear.

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 must carry the full behavioral burden. It discloses that only provided fields are updated (partial update), supports null to clear fields, and lists possible values for enums. However, it doesn't mention authorization, side effects, rate limits, or what happens on conflict. Given the tool's complexity (14 params, nested objects), the description is adequate but not exhaustive.

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 well-structured: a clear introductory sentence, a required field note, a block listing optional fields with explanations, and an example. Each sentence adds value, and the length is appropriate for the number of parameters. No 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?

The description covers most important aspects: required and optional fields, null behavior, example usage. However, it lacks explicit mention of the return value (output) upon update. With no output schema, the description should ideally state what is returned. Despite this, the description is fairly complete for a complex 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 baseline is 3. The description adds value by summarizing fields, explaining null behavior, and providing a concrete example. It also clarifies convenience fields like assignee and assignment. This goes beyond 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 'Update an existing test plan in TestCollab.' This distinguishes it from sibling tools like create_test_plan, get_test_plan, list_test_plans, and delete_test_plan. The verb 'update' and resource 'test plan' are specific and unambiguous.

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 specifies that 'id' is required and that 'all other fields are optional and only provided fields are updated.' This implies partial update behavior and hints at when to use this tool (modify existing plan). While it doesn't explicitly state alternatives for creating or reading, the sibling tool names provide enough context.

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