allure-testops-mcp

Server Quality Checklist

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Latest release: v0.8.2
Disambiguation5/5
Each tool targets a distinct entity or operation (projects, statistics, test cases, launches, results, failures, custom fields, layers, statuses) with clear boundaries and explicit 'Don't use when' guidance to prevent misselection.
Naming Consistency5/5
All tools follow a consistent 'allure_verb_noun' pattern (e.g., allure_list_projects, allure_get_test_case), making the naming predictable and easy to navigate.
Tool Count5/5
With 11 tools, the set is well-scoped for a test management server, covering essential query and list operations without being overwhelming.
Completeness3/5
The tools are heavily read-focused, missing create/update/delete operations for test cases and other entities, which are fundamental to full lifecycle management. The descriptions reference 'allure_create_test_case' but it is not included.
Average 4.7/5 across 11 of 11 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 75 commits in the last 12 weeks
- Last stable release on June 5, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, destructiveHint, idempotentHint, and openWorldHint, so the description adds value by detailing the returned fields and the negative/positive id distinction for built-in vs. custom fields.
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 four sentences, each serving a purpose: stating the action, listing return fields, giving usage guidance, and providing extra id context. It is front-loaded and efficient.
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?
For a tool with one parameter, clear annotations, and an output schema (implied), the description is complete. It explains what the tool returns, when to use it, and references a related sibling tool, leaving no gaps.
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 100% with a clear description for 'project_id'. The description does not add further parameter-level semantics but provides useful context about the tool's output, resulting in a baseline score of 3.
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 'List' and the resource 'custom fields defined on a project', and differentiates from sibling tools by referencing 'allure_get_test_case_custom_fields' as a subsequent step. It also explains the output specifics (field_id, name, single_select, required) and the id sign convention.
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 recommends using this tool 'before reading a test case's values with allure_get_test_case_custom_fields', providing clear context. While it does not mention when not to use it, the recommendation is strong and helps the agent 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?
Annotations already provide readOnlyHint, destructiveHint, idempotentHint, openWorldHint. The description adds value by detailing the return structure (dict with project_id, count, layers) and the negative id behavior for built-in layers.
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 extremely concise: two sentences plus a return format note. No unnecessary words, every sentence adds value.
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?
The tool has one parameter with full schema coverage, output schema exists, and the description covers usage guidance, return structure, and special behavior (negative ids). It is complete for a simple 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 100% with a clear description for 'project_id'. The tool description does not add any further parameter semantics beyond what the schema already provides, so baseline 3 is appropriate.
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 'List' and the resource 'test layers defined in a project', and specifies the output (id, name). It also differentiates from sibling list tools by focusing on layers.
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 says 'Use this to discover valid layer names/ids before setting a layer on allure_create_test_case / allure_update_test_case', providing a clear use case and hinting at alternatives. It also notes built-in layer id ranges.
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?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that the tool performs 3-4 API calls with progress reported via MCP Context, which is useful behavioral insight beyond the annotations. No contradictions.
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 (summary, Args, Returns, Examples, Don't use). It is front-loaded with the purpose. While somewhat verbose, every section adds value and is easy to parse.
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?
The description fully compensates for the lack of an output schema by providing a detailed return dict specification. It includes examples and instructions on interpreting results. For a simple one-param tool, it is exceptionally 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?
The sole parameter (project_id) is well described in the schema and the description adds examples of usage. With 100% schema coverage, the baseline is 3, and the description adds marginal value but not significant new meaning.
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 'Get summary statistics for an Allure project' and lists specific outputs (TC count, automation rate, last launch breakdown). It distinguishes from siblings by noting when not to use it (for per-test detail or full launch history, referencing sibling tools).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit 'Don't use when' instructions with alternative tools (allure_get_test_results, allure_list_launches) and includes concrete examples for typical queries. This gives clear guidance on appropriate vs. inappropriate usage.
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?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds behavioral context about the flattened return structure (field_id, field_name, value_id, value_name), which is not in annotations. No contradictions.
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, no fluff, and front-loaded with the main action. Every sentence adds value.
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 tool has only one parameter, high annotation coverage, and an output schema, the description is complete. It explains the return structure and differentiates from a sibling.
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 100% (only parameter 'test_case_id' described). The description does not add parameter-specific info beyond the schema, but it explains the return format. Baseline 3 is appropriate since schema covers the parameter.
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 the custom-field values set on a test case.' It specifies the exact action and resource, and distinguishes from sibling 'allure_get_test_case' by noting that custom fields are not included there.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells when to use this tool: 'fetch them here when you need a test case's custom-field assignments.' It also mentions that these are not included in 'allure_get_test_case', providing clear differentiation from a sibling tool.
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?
Annotations already declare readOnlyHint, destructiveHint, idempotentHint, and openWorldHint. The description adds value by specifying the return structure (dict with project_id, count, statuses) and mentioning built-in statuses use negative ids, which aids understanding but does not heavily extend beyond the annotations.
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 short paragraphs with the main action front-loaded. Every sentence serves a purpose: defining the function, guiding usage, and explaining return format. No unnecessary words.
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 (one integer parameter, no nested objects) and the presence of an output schema, the description covers the tool's purpose, usage, and return values completely. It leaves no important gaps.
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 100% for the single parameter project_id, so baseline is 3. The description does not add additional meaning beyond what the schema provides ('Allure project ID'). No extra semantics are offered.
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 lists test-case statuses with specific fields (id, name, color). It distinguishes from sibling tools like allure_list_projects and allure_list_test_cases because it focuses on statuses, not other entities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells when to use the tool: 'Use this to discover valid status names/ids before setting a status on allure_create_test_case / allure_update_test_case.' It also notes that built-in statuses use negative ids, providing crucial context.
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?
Annotations already indicate read-only, non-destructive, idempotent behavior. The description adds valuable detail: it returns a dict with specific keys, including 'steps' with 'depth' marker, and clarifies that steps are empty when 'include_scenario' is false. This goes beyond annotations without contradicting them.
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 well-structured: a clear first sentence stating purpose, a contrast with a sibling, and a concise return format. It is appropriately sized—every sentence adds value without unnecessary fluff.
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 presence of an output schema, the description complements it well by listing the dictionary keys and explaining the step structure. It covers the essential aspects of the tool's behavior and return value comprehensively.
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?
Input schema covers both parameters with descriptions. The description adds context about the structure of steps and the effect of 'include_scenario' (an extra call), which is not in the schema. Since schema coverage is 100%, baseline is 3, but the extra context justifies a 4.
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 'Get one test case's full detail' and lists specific fields (status, layer, tags, steps). It explicitly distinguishes from the sibling 'allure_list_test_cases' which returns summaries, making it easy for an agent to select the correct tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly contrasts with 'allure_list_test_cases' and says 'Use it to read or review the actual content of a test case.' It also explains the effect of 'include_scenario', providing clear when-to-use and when-not-to-use guidance.
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?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool's safety profile is known. The description adds value by detailing the return structure (pagination block, launch breakdown) and pagination behavior, which goes beyond annotations.
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 well-structured with clear sections (Args, Returns, Examples, Don't use when) and is concise. Every sentence adds necessary information without redundancy.
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 tool's complexity (pagination, detailed return structure), the description covers all key aspects: parameter details, return format with field descriptions, usage examples, and sibling differentiation. It is complete for an agent to correctly select and invoke the tool.
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?
Input schema has 100% description coverage, so parameters are well-documented. The description adds a usage hint for the 'size' parameter ('20 is usually enough for triage'), which provides additional context 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 action ('List recent launches'), the resource ('launches for a project'), and ordering ('newest first'). It also distinguishes from sibling tools by explicitly stating alternatives in the 'Don't use when' section.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use the tool (to list launches with breakdown and pagination) and when not to use it, naming two alternative tools (allure_get_test_results, allure_search_failed_tests). This clearly helps the agent decide between siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive. The description adds detailed return structure (fields like id, name, status, duration_ms, error truncated to 300 chars) and pagination behavior, surpassing what annotations offer.
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?
Description is concise and well-structured: purpose sentence, Args with bullet-like notation, Returns dict, Examples, and exclusion note. No wasted words, and critical information is front-loaded.
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 tool has 4 parameters, an output schema, and sibling tools, the description covers all aspects: parameter details, return format, usage hints, and alternatives. It enables correct invocation without gaps.
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 100%, so baseline is 3. The description adds meaning by linking launch_id to another tool's output, explaining page is 0-based, size limits, and providing real-world examples. That justifies a 4.
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 retrieves individual test results filtered by status, with a specific verb 'Get' and resource 'test results inside a launch'. It distinguishes from sibling 'allure_search_failed_tests' by noting that sibling handles FAILED+BROKEN together.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when not to use (for FAILED+BROKEN) and points to alternative 'allure_search_failed_tests'. Also provides examples clarifying common use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds context beyond annotations by describing pagination parameters and the return structure (dict with count and list of projects with id, name, abbreviation). No contradiction with annotations.
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?
Well-structured with sections, examples, and anti-guidance. Every sentence adds value; no waste.
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?
Complete description for a simple listing tool: covers usage, return format, examples, and when not to use. No output schema needed.
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 100%, but the description adds value by explaining the purpose of parameters in the context of project discovery and providing examples of usage.
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 all projects in the Allure TestOps instance. It uses specific verb 'list' and resource 'projects', and distinguishes from siblings by noting this tool discovers project IDs used by other tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states to use this first to discover project IDs and advises not to use when the project ID is already known, with a clear alternative of going directly to the target tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses critical behavioral details beyond annotations: audit fields and tags are only populated when owner is set (Allure endpoint constraint), owner is actually a union of createdBy and lastModifiedBy, the automated filter becomes client-side when owner is used, pagination implications, and the regex pattern preventing RQL injection. No contradiction with annotations.
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 well-structured with Args, Returns, Examples, and a 'Don't use when' section. It is front-loaded with the primary purpose, and every section earns its place by providing necessary context without redundancy.
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 tool's complexity (5 parameters, optional filters, server-side vs client-side behavior), the description covers all aspects: input semantics, output structure, edge cases (owner limitation), and practical examples. The presence of an output schema does not reduce the value of the detailed return format explanation.
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?
Despite 100% schema description coverage, the description adds significant semantic value: explains why owner maps to creator/modifier, details the trade-off with automated filtering, provides pattern explanation and injection prevention context, and gives concrete examples for using automated, page, and size parameters.
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 test cases for a project with optional automation and ownership filters. It details specific fields returned (id, name, automated, status, layer, etc.), distinguishing it from sibling tools like allure_get_project_statistics, which returns summary data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage examples (e.g., 'How many manual TCs does project 63 have?'), a 'Don't use when' section pointing to allure_get_project_statistics for automation percentages, and detailed context on when to set owner and associated trade-offs (e.g., client-side filtering).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent. The description adds that up to 3 API calls are made and progress is reported via MCP Context, which is useful beyond annotations.
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?
Structured with summary, usage, args, returns, examples, and exclusions. Every sentence is purposeful and the description is appropriately sized for the tool's complexity.
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 presence of output schema, the description still explains return values and provides examples. It covers all necessary context for correct usage and triage.
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 coverage is 100%, but the description adds meaning: max failures per status (2*limit total), launch_id fallback to latest, and mentions ctx auto-injection. This adds value 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 finds FAILED and BROKEN tests in a launch. It uses a specific verb and resource, and distinguishes from sibling tools like allure_get_test_results by focusing on failures.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance is provided: use for triage, with examples. A 'Don't use when' section specifies when to use alternative tools (allure_get_test_results). Context and alternatives are clearly stated.
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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