testrail-mcp

by Sergey-Bl

Overview Schema Source Related Servers Score Discussions Admin

Python

Remote

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.3.1
Disambiguation4/5
Most tools have clearly distinct purposes, but the high number of 'prepare_*' tools (prepare_cases_from_confluence, prepare_cases_from_jira, prepare_coverage_gaps, etc.) could cause confusion for an agent trying to select the right one, despite detailed descriptions.
Naming Consistency4/5
The naming convention is predominantly verb_noun in snake_case, but there are minor inconsistencies such as 'add_test_cases_bulk' (should be 'add_bulk_test_cases') and 'flaky_test_detector' (noun phrase instead of verb_noun).
Tool Count2/5
With 31 tools, the server exceeds the recommended range for well-scoped MCP servers. While TestRail is a complex domain, several tools could be merged or omitted to reduce cognitive load.
Completeness3/5
The server covers core CRUD for runs, cases, and results, and adds valuable analysis tools. However, it lacks deletion capabilities (delete_case, delete_run) and project creation, which are notable gaps for full lifecycle coverage.
Average 3.5/5 across 31 of 31 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 1 community issues answered or closed in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavior. It states that the tool returns tests with current status, but does not explain pagination (limit parameter), ordering, or whether it is read-only. The minimal description leaves ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While extremely concise, the description is too brief to be effective. It is a single phrase rather than a full sentence, and lacks structure to convey important details like pagination.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description omits details about pagination, ordering, error handling, and the exact meaning of 'current status.' Given the tool's two parameters and the existence of similar siblings, the description is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. However, it provides no explanations for the 'run_id' or 'limit' parameters beyond what is in the schema (e.g., run_id as integer, limit default 250). This fails to help the agent understand their semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool retrieves tests in a run along with their current status (latest result). It distinguishes itself from sibling tools like 'get_results_for_run' by focusing on tests rather than raw results.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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, nor does it mention prerequisites or context such as the need for a valid run_id.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing behavioral traits. It states that the tool creates a test run and modifies state, but does not disclose authentication needs, rate limits, side effects, reversibility, or destructive potential. This is insufficient for an agent to assess risk.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two concise sentences without extraneous information. The purpose is front-loaded, and the behavioral nuance is presented efficiently. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having 8 parameters and no output schema, the description does not explain the return value, prerequisites, or behavior of omitted parameters. For a creation tool, an agent would need to know what the response contains and how to handle optional fields. The description is incomplete for reliable invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, requiring the description to compensate. It only explains the 'include_all' and 'case_ids' parameters, leaving the other six parameters (name, refs, suite_id, project_id, description, milestone_id) completely undocumented. The description adds some value but fails to cover the majority of parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool creates a new test run, which is a specific verb-resource combination. It provides additional detail about default inclusion of all cases and scoping, which adds clarity. However, it does not explicitly differentiate from sibling tools like 'update_run' or 'add_result', so it falls short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers guidance on parameter use (include_all and case_ids) but does not specify when to use this tool versus alternatives. There is no mention of appropriate contexts, prerequisites, or exclusions, leaving the agent without critical decision-making information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It adds one behavioral trait—irreversibility—but omits permissions, side effects, state prerequisites, or response behavior. Minimal disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, no filler. Purpose is stated immediately, and the key behavioral note is appended efficiently. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no output schema and no annotations, the description lacks detail on post-closure behavior, prerequisites, and typical usage flow. Leaves significant gaps for agent decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and description does not explain the run_id parameter beyond the name. Agent must guess its meaning and source. No compensation for missing schema detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool marks a test run as completed/archived, which directly matches the tool name 'close_run'. It distinguishes from siblings like create_run or update_run by specifying the finalizing action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use vs alternatives like update_run. The irreversibility hint is present but not framed as a usage decision criterion. Lacks when-not or prerequisite context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It does not mention return value, side effects (e.g., creation is irreversible?), authentication needs, or error conditions. For a create operation, the lack of output or confirmation 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: one sentence for purpose, then a code block example. Information is front-loaded. The example is well-placed, though it could be slightly more compact with a note that `case` is an object.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations, output schema, and the presence of a nested object parameter, the description should provide more context: what the tool returns after creation, error handling, and required permissions. It meets the minimum for a simple creation but leaves important gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no property descriptions for the `case` object (additionalProperties: true), and the description offers a helpful example structure with `title`, `preconditions`, and `steps`. However, it does not describe the `section_id` parameter or other possible fields in `case`. This adds some value but is incomplete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create') and the resource ('a single test case in a TestRail section'). The word 'single' hints at differentiation from bulk siblings. It is 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.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus siblings like add_test_cases_bulk (bulk creation) or update_case (modification). The description does not mention prerequisites, such as requiring an existing section or the context for adding cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description only hints at read behavior. It does not disclose whether pagination applies (despite a limit parameter), rate limits, ordering, or data freshness. 'Includes comments, defects, who tested when' is helpful but insufficient for full behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, front-loaded sentence with zero waste. Every word adds value. Ideal conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 parameters, output schema present), the description covers the basic purpose and content. However, missing guidance on the limit parameter and lack of behavioral details (pagination, ordering) leave gaps. Adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning for 'run_id' by explaining it identifies a run, but it ignores the 'limit' parameter entirely. With 0% schema description coverage, the description should compensate, but it fails to explain how limit controls pagination or result size.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the verb ('get') and resource ('results for a run'), and specifies what is included ('comments, defects, who tested when'). It is distinct from sibling tools like add_result or get_run, but does not explicitly differentiate from other retrieval tools like get_tests_in_run.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The agent is not told which scenarios favor this over get_tests_in_run or get_run, nor any prerequisites or contextual advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description bears full responsibility for behavioral disclosure. It only states 'Fetch', implying a read operation, but omits details like authorization needs, rate limits, or what 'full metadata' entails.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, which is concise but lacks sufficient detail to be fully helpful. It is front-loaded but incomplete.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and low schema coverage, the description fails to explain what 'full metadata' includes or the return format. Compared to sibling tools, it is inadequately informative.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description should compensate, but it adds no meaning beyond the parameter name 'run_id'. No format, source, or usage context is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Fetch'), the resource ('full metadata of a single test run'), and effectively distinguishes from sibling tools like 'get_results_for_run' or 'get_tests_in_run' which return more specific data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, such as prerequisites or limitations. The description does not mention when to use other related tools like 'get_results_for_run'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits. It only states it lists runs and explains two filters. It omits details on pagination, ordering, default behavior (e.g., all projects if project_id not set), or any 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences plus a bullet list. Front-loaded with purpose, concise, every sentence adds value. No waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 4 parameters, no required, no enums, low complexity. Description gives purpose and two filter specifics but omits explanation of limit and project_id. No mention of default behavior or pagination. Incomplete for a listing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaning for suite_id and is_completed, explaining their values. However, it does not explain limit or project_id, which are also important. Thus, it partially compensates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List test runs in a project.' which is a specific verb+resource. It also explains two filters, adding context. However, it does not explicitly differentiate from sibling tools like get_run or get_results_for_run, but the name and filter info help.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. It implies usage through filters but does not mention when not to use it (e.g., for single run retrieval) or any prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Description only says 'Fetch' and 'Returns context', implying read-only behavior, but does not disclose potential side effects, permission requirements, error handling, or what happens if the ticket is not found. Significant gaps remain.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is very short (two sentences), but omits important information. It is not front-loaded with key details; the example is helpful but not sufficient. Could be restructured to include parameter overview or usage hints without significantly increasing length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has 8 parameters, no output schema, and no annotations. Description only covers fetching Jira ticket and house style anchors, but does not explain return values, behavior of optional parameters, or error conditions. Incomplete for an AI agent to use reliably.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning no parameter descriptions exist in the schema. Description only mentions issue_key with an example, ignoring other critical parameters like suite_id, project_id, section_id, house_style, etc. This fails to compensate for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'Fetch' and resource 'Jira ticket + house-style anchors', and indicates purpose is to return context for generating TestRail cases. This distinguishes it from siblings like prepare_cases_from_confluence and prepare_cases_from_text which use different sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies usage when needing to generate TestRail cases from a Jira ticket, but does not explicitly state when to use or not use this tool vs alternatives. No exclusions or prerequisites are mentioned, though the context signal helps infer it's for Jira-based case generation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility. It only states it returns cases, implying a read operation, but fails to disclose any behavioral traits like whether it modifies data, requires permissions, or how cases are selected.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two clear, front-loaded sentences. Every word serves a purpose—the first defines functionality, the second provides usage guidance. No unnecessary content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite low complexity, the description leaves significant gaps: it does not define 'style-example format', explain how to specify the target section among multiple parameters, or describe the output structure. With no output schema and no annotations, the description is insufficient for complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the input schema provides no parameter descriptions. The tool description does not mention any of the 5 parameters (n, suite_id, project_id, section_id, section_hierarchy), failing to add meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns existing cases from a target section in style-example format. The verb 'Return' and resource 'existing cases' are specific. No sibling tool has a similar purpose, so differentiation is implicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The second sentence explicitly advises using it to preview house style before generating, providing a clear context of use. However, it does not mention when to avoid using it or suggest alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description clearly states that no LLM call happens server-side, which is a key behavioral trait. It also explains the output (spec, case titles, instructions). This adds value beyond the name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loading the main purpose. It is concise but slightly run-on; could be split for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given three parameters and no output schema, the description is incomplete. It omits details about the output format, the optional 'feature_title', and constraints on inputs (e.g., what constitutes valid 'cases').
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description only loosely implies that 'spec_text' is the spec and 'cases' are existing case titles. The 'feature_title' parameter is not mentioned at all. The description does not compensate for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states that the tool returns the spec text, existing case titles, and instructions for coverage gap detection. It distinguishes itself by noting that no server-side LLM call occurs. However, the verb 'prepare' is vague and the core action is returning data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus its many siblings (e.g., prepare_cases_from_text, prepare_feature_bootstrap). The description implies it's for coverage gap analysis but doesn't provide when-not-to-use or alternative contexts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explicitly states that no server-side LLM call happens, which is a key behavioral trait. However, without annotations, it should disclose more (e.g., side effects, permissions). The single behavioral detail helps but is insufficient for full transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no redundancy, and the first sentence immediately states the tool's core purpose. Efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description provides the essential purpose and a key behavior, but lacks details on output format, parameter semantics, and expected usage patterns. Given no output schema and only 2 parameters, it is minimally adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description should compensate. It mentions 'case batch' and 'lint instructions' but does not explain the 'cases' array format or 'feature_title' purpose. The description adds minimal value beyond the schema's parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it returns a case batch with lint instructions for client-side evaluation, which is clear. It distinguishes from sibling tools that fetch cases from external sources, but could be more precise about what 'lint instructions' entail.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like prepare_cases_from_confluence or prepare_cases_from_jira. The description implies it's for client-side evaluation, but does not explicitly state selection criteria or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavior. It only states the action without disclosing return format, error handling, or whether additional permissions are needed. For a read operation, basic safety is assumed but not stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no redundancy, but front-loads purpose. Could be slightly more structured with parameter details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple fetch tool with one parameter, the description is minimally adequate. However, it lacks context on return value, error cases, and when to prefer this over sibling tools, given no output schema or annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description explains that case_id is the TestRail ID, adding meaning beyond the schema's type definition. However, it lacks examples or constraints like validation or required status (already in schema), so minimal extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it fetches a single test case by TestRail ID using the verb 'fetch' and resource 'test case', which is specific and distinguishes from sibling tools like create_test_case or search_test_cases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like search_test_cases or get_tests_in_run. It lacks explicit context or exclusions for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only mentions fetch and return, lacking behavioral details like side effects, error handling, or read-only nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Efficient two-sentence structure with front-loaded purpose, but could add more value without significant length increase.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 8 parameters, no output schema, and no annotations, the description lacks explanation for most parameters and return value structure, leaving significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only page_id is explained with format guidance; the other 7 parameters lack any description, and schema coverage is 0%, so the description fails to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool 'Fetches a Confluence page + house-style anchors' and specifies the return purpose ('context to generate TestRail cases'), distinguishing it from siblings like prepare_cases_from_jira.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage for generating TestRail cases from Confluence, but no explicit guidance on when to use this vs alternative prepare_cases_from_* tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 discloses the constraint and parameter default, but does not mention side effects, permissions, error behavior, or return values. This is insufficient for a creation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no extraneous information. The purpose and key constraint are front-loaded. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description should cover more. It lacks return behavior, error conditions, and examples. The tool is simple but the description leaves gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add value. It provides an additional detail for project_id (environment variable default) but gives no extra meaning for name or description beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Create' and the resource 'new TestRail suite in a project', with a specific constraint 'only works on multi-suite projects'. This distinguishes it from sibling tools like list_suites and create_test_case.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage only for multi-suite projects, but does not explicitly state when not to use it or suggest alternative tools. The guidance is implied but not comprehensive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only states the return format and default behavior. It does not disclose potential side effects, authentication requirements, rate limits, or any other behavioral traits. For a listing tool, read-only is implied but not confirmed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise at two sentences, with the purpose stated first. Every sentence adds value, and there is no superfluous text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, the description need not explain return values. However, for a tool with three parameters and no annotations, the description should provide more context about when and how to use each parameter. It mentions env defaults but lacks full parameter coverage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description should explain parameters. It only notes that project_id and suite_id default from env variables, but does not describe what limit does or explain the meaning of suite_id and project_id. The three parameters are largely left unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists sections in a suite and specifies the return format {id, name, parent_id}. This distinguishes it from sibling tools like create_suite or list_suites.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions that project_id and suite_id default to environment variables when omitted, which provides some usage guidance. However, it does not explicitly say when to use this tool versus alternatives (e.g., get_or_create_section), and no when-not-to-use instructions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains the algorithm (token overlap, no embeddings) and the output format. It does not explicitly state that the tool is read-only or describe side effects, but the behavior is reasonably transparent for a deduplication function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with two short paragraphs. The purpose is stated first, followed by algorithmic details and output structure. No superfluous text. It could be slightly more compact, but it is well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, the description adequately explains the return format. It covers the algorithm, threshold parameter, and lists the output fields. It omits details about the required fields in `cases` and the exact role of `section_id`, but overall it is complete enough for an AI agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It explains `threshold` (with range and purpose) and implies the purpose of `cases` and `section_id`. However, `suite_id` and `project_id` are not mentioned, leaving gaps. The description adds value for some parameters but is incomplete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks generated cases against existing ones in a TestRail section using title-token overlap. The verb 'check' is somewhat generic, but the context makes the purpose clear. It does not explicitly differentiate from sibling tools like search_test_cases, but the purpose is distinct enough.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have generated cases and need to deduplicate against a section. However, it offers no guidance on when not to use it or alternatives among siblings. The context is clear but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the core mutation (creating missing parents) and the return format. However, it does not cover potential side effects, idempotency, error conditions, or required permissions. Given no annotations, this 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three short sentences efficiently convey purpose, return format, and defaults. No redundant words; the most critical information (hierarchy example) is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given moderate complexity (3 params) and no output schema, the description covers key aspects: purpose, example, return type, and env defaults. It lacks detail on error handling and idempotency but is reasonably complete for a 'get or create' utility.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds value by explaining the hierarchy parameter with an example and noting environment variable defaults for project_id and suite_id. However, it does not specify validation rules, allowed characters, or confirm that suite_id and project_id are optional.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool resolves a section hierarchy and creates missing parents, with a concrete example ('Auth > Login > Edge Cases'). It distinguishes from siblings like list_sections (which only lists) by specifying the creation behavior.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like list_sections or create_test_case. There is no mention of prerequisites, when not to use it, or how it differs from similar tools in the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses patch behavior (partial update) but omits details about permissions, idempotency, or 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that is concise, front-loaded, and contains no extraneous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without annotations or output schema, the description is adequate for a simple patch operation but lacks details on return values or prerequisites.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description adds no meaning to individual parameters beyond the patch hint. Baseline fails to compensate for low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool patches an existing test run's metadata, using a specific verb and resource. It distinguishes from siblings like create_run and close_run.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Pass only fields you want to change' gives implicit guidance for partial updates, but no explicit when-to-use or alternatives are mentioned, leaving room for confusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It explains parameter formats (status, elapsed, defects) but does not disclose behavioral traits like mutability, error handling, or side effects. Lacks explicit mutation warning.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise with front-loaded main purpose. Each line adds value, using efficient formatting (newlines and backticks). No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with 7 parameters, no annotations, and no output schema, the description covers parameter formats but lacks behavioral context (e.g., what happens on error, success, or invalid inputs). Not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description must compensate. It explains status (accepted strings and IDs), elapsed (time format), and defects (comma-separated), but does not explain run_id, case_id, comment, or version. Partial coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Post a single test result by case_id' with a specific verb and resource, and distinguishes from siblings like 'add_bulk_results' (multiple) and 'get_results_for_run' (retrieval).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied usage for single test results, but no explicit when-to-use, when-not-to-use, or comparison with siblings like 'add_bulk_results' or 'get_results_for_run'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It usefully states that no LLM call happens server-side, but does not disclose whether the tool is read-only, destructive, or requires authentication. Expected behavior beyond the main action is sparse.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two compact sentences that front-load the core action and then provide ordering and server-side behavior. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 2 simple parameters and no output schema, the description covers the basic purpose and ordering. However, it does not describe the format of the returned structured data or the nature of the 'narrate instructions', leaving gaps for complex usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds the semantics that run_id_a is the baseline (older) and run_id_b is the newer run. This adds meaning beyond the schema, but still lacks details on how to obtain these IDs or any constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'compute' and the resource 'regression / fix / coverage delta between two runs', and specifies it returns structured data and instructions. It distinguishes from siblings by mentioning the diff purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains that A is baseline and B is newer, providing ordering context. However, it does not tell when to use this tool over alternatives like prepare_coverage_gaps or prepare_run_summary, nor does it mention prerequisites or when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description lacks detail on error conditions, authentication needs, or side effects beyond the patch behavior. The forwarding of `fields` as-is is mentioned but overall transparency is minimal for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences plus an example, front-loaded with the core action, no wasted words. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple update tool with two parameters, but lacks information on return values or error states. Given no output schema, the description could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates well by explaining that `fields` is forwarded as-is, listing common keys, and providing an example for `custom_steps_separated`. This adds significant meaning beyond the plain schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Patch' (update) and resource 'existing test case', and shows how `fields` is forwarded to TestRail as-is, distinguishing it from sibling tools like create_test_case or get_test_case.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides usage guidance by listing common keys and how to structure steps, but does not explicitly state when to use this tool vs alternatives or specify exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavioral traits. It mentions speed but omits error handling, batch limits, and whether partial failures occur, leaving gaps for safe agent invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with purpose, each sentence provides essential information without redundancy or wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a bulk mutation tool with no output schema and no annotations, the description fails to cover error behavior, response format, or usage constraints, making it incomplete for robust agent decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Adds meaning beyond schema: explicitly states required fields (case_id, status) and optional fields for each entry in results, compensating for 0% schema description coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the verb 'Post many results' and resource 'TestRail results', distinguishing from sibling 'add_result' by emphasizing speed advantage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied usage for bulk posting vs looping add_result, specifies required fields per entry, but lacks explicit when-not or alternative contexts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description adds flakiness criteria and output format, but doesn't state read-only nature or side effects. Partial transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences plus definition. Zero waste, front-loaded with action and criteria.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema; description mentions 'run-by-run trace' which provides expected output. Could be more detailed on trace format, but sufficient for a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage 0%. Description explains case_id and last_n_runs (N) but does not mention project_id parameter. Adds some value beyond schema but incomplete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it pulls a test case's result across multiple runs and flags flakiness, with specific definition. Distinguishes from sibling tools like get_results_for_run.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied use for flakiness detection, but no explicit when-to-use vs alternatives like get_results_for_run or get_test_case. Missing guidance on prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that project_id defaults to TESTRAIL_PROJECT_ID from env when omitted or 0, providing key behavioral context. However, no annotations exist, and the description doesn't confirm read-only or safety aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the action and parameter default. No extraneous information; every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 parameter) and the presence of an output schema, the description covers the essential behavior. Could mention what suites are returned (e.g., all or filtered), but not a major gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description adds crucial meaning: project_id defaults to env variable when omitted or 0. This clarifies parameter behavior beyond the schema definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List suites under a TestRail project,' specifying the verb and resource. It distinguishes from sibling tools like list_projects, list_runs, and list_sections by focusing on 'suites'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like search_test_cases or list_runs. The description doesn't mention when not to use it or provide context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the scanning order (TestRail order) and that it returns the first matching section. It implies a read-only operation (no mention of modification), which is appropriate for a query tool. No contradictions or missing critical behaviors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no extraneous words. The key action and condition are front-loaded in the first sentence. Every phrase adds value. It is highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters and no annotations, the description should provide more context. It fails to explain the purpose of suite_id, project_id, and scan_limit, nor does it address what happens when no section meets the threshold (e.g., returns null). The output schema exists but is not shown, so the description could still add value by describing return behavior. These gaps make it incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain the parameters. Only min_cases is mentioned ('at least min_cases real cases'). The remaining parameters (suite_id, project_id, scan_limit) are not explained, leaving the agent to infer their purpose from context. This is insufficient for a 4-parameter tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Find'), resource ('section'), and condition ('at least min_cases real cases'). It also specifies the use case ('house-style anchor when bootstrapping a new feature') and return behavior ('first section meeting the threshold'), distinguishing it from siblings like list_sections.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a specific use context ('useful as a house-style anchor'), but does not explicitly state when not to use the tool or compare it to alternatives such as get_or_create_section. The guidance 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states it lists projects visible to the configured user. It does not disclose potential limitations like pagination, caching, or whether it returns only active projects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, well-structured sentence that communicates the entire purpose without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and an output schema exists (covering return format), the description provides sufficient context for its simplicity. It could briefly mention that it returns a list of project objects, but that is covered by the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so baseline is 4. The description correctly does not include parameter info, and the input schema already covers everything.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (list), the resource (all TestRail projects), and the scope (visible to the configured user). It effectively distinguishes from sibling tools like list_runs or list_suites.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage (when you need a list of projects) but provides no explicit guidance on when to use this tool versus alternatives like list_runs or list_suites, nor any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the read nature (listing) and env defaults, but does not mention rate limits, permissions, result ordering, or other behavioral traits. Adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two well-structured sentences: first states the purpose, second explains defaults. No extraneous information, front-loaded with key action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters, 0% schema coverage, and an output schema (not shown), the description covers the core use case well. It explains the listing action and filters, but could mention limit or pagination behavior. Overall, complete enough for a list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It names four of five parameters (project_id, suite_id, section_id, title_contains) but omits 'limit'. It adds context about env defaults but does not explain each parameter's meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List test cases in a project' with optional filters. This distinguishes it from sibling tools like get_test_case (single) or add_test_cases_bulk (create).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides guidance on default values: 'Both project_id and suite_id fall back to env defaults when omitted or 0.' This helps agents know when to omit these parameters. However, it does not explicitly state when not to use this tool or suggest alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses creation behavior, the expected shape of cases items, mapping to TestRail format, and targeting with auto-creation of missing hierarchy nodes. However, it omits potential errors or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with three short paragraphs, each adding essential information. The purpose is front-loaded, and the bullet-like list for cases structure is clear. No extraneous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity, lack of output schema, and no annotations, the description covers input format and targeting well. It explains the core behavior but could detail the return format further or address error scenarios. Still adequate for an agent to use effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must add meaning. It explains the complex `cases` parameter structure and the `section_id`/`section_hierarchy` targeting. The `suite_id` and `project_id` parameters are not described but have defaults. Overall, it significantly enhances understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: push a batch of test cases to TestRail and return the IDs created. It effectively differentiates from siblings like create_test_case (single) and add_result (results).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains how to target sections (section_id or section_hierarchy) but does not provide explicit when-to-use or when-not-to-use guidance compared to alternatives like create_test_case. The usage context is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explicitly states that no LLM call happens server-side, that the tool fetches specs and anchors, and returns data for client-side generation. This clarifies what the tool does and does not do, providing adequate transparency about its behavior and side effects (none).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a single sentence summarizing the purpose, a clear statement about no server-side LLM, then a bullet list of required and optional parameters. Every sentence adds value without repetition. It is front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 7 parameters, no output schema, and no annotations, the description provides substantial context: the workflow, parameter meanings, and a recommendation for style_from_suite_id. It explains what the tool returns (data for client to drive generation) but does not specify the output structure. The omission of project_id is a gap, but overall it is quite complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains the meaning and allowed values for source_type, source_value, section_name (hierarchy allowed), new_suite_name vs existing_suite_id, and style_from_suite_id (recommendation). However, it omits project_id entirely, which is a parameter in the schema. Thus, it adds value for most parameters but leaves one undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool prepares a feature-import workflow by fetching specs and house-style anchors, returning data for client-driven generation and push. It distinguishes from siblings like prepare_cases_from_confluence by outlining a two-step process (this tool, then add_test_cases_bulk). The verb 'prepare' and resource 'feature bootstrap' are specific and well-defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists required and optional parameters and provides context on when to use each (e.g., new_suite_name vs existing_suite_id, style_from_suite_id recommended for new empty suites). It details the workflow: call this tool, then add_test_cases_bulk. However, it does not explicitly exclude scenarios where direct import tools like prepare_cases_from_confluence would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 discloses that the tool returns the spec, house-style anchors, target section, and an instructions block. It explains house-style matching and targeting modes. It does not mention any destructive actions or side effects, but as a fetch/prepare tool, this is acceptable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections and bullet-like formatting. It is front-loaded with the main purpose. Slightly verbose, but every sentence adds value. Could be more concise, but still effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 9 parameters, no output schema, and no annotations, the description provides substantial context. It explains the return content, the MCP-native pattern, targeting modes, house-style matching, and how to override defaults. It is fairly complete for a preparation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds significant meaning beyond the schema by explaining the purpose of section_id vs section_hierarchy, house_style, house_style_section_id, and the defaults. It also explains how the house_style_examples parameter works.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it fetches inputs for generating TestRail cases from a free-form spec, distinguishing it from siblings like prepare_cases_from_confluence and prepare_cases_from_jira. The verb 'Fetch' and resource 'inputs needed to generate TestRail cases' 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.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the tool (to get inputs for case generation) and details targeting modes and house-style matching. It provides context on how to use it, but does not explicitly list when not to use it or contrast with all sibling tools. However, the guidance is sufficient for an agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully handles transparency. It discloses that no server-side LLM call occurs and that Claude in the client writes the report. It does not mention side effects or auth needs, but given the read-only nature of the tool, this is acceptable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, each earning its place: purpose, architectural note, and parameter guidance. No fluff, well structured, and front-loaded with the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (2 parameters, no output schema), the description covers all needed aspects: what the tool does, how it works architecturally, and parameter usage. It is sufficiently complete for an AI agent to select and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining the purpose of 'include_passed_titles' and providing guidance on when to set it to true. It adds significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it pulls run metadata, statuses, and failure comments and returns a structured payload with report instructions. It uses specific verbs ('pull', 'return') and identifies the resource, making it easy to distinguish from siblings like get_run or get_results_for_run.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context on when to use the boolean parameter and the default behavior, and it explains that no LLM call happens server-side. It implicitly suggests use for summary generation but does not explicitly state when not to use it or list alternatives. Still, it gives actionable guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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