io.github.phoenice-labs/universal-test-framework

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by phoenice-labs

Overview Schema Related Servers Score Discussions

Python

Hybrid

Server Quality Checklist

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Latest release: v1.0.3
Disambiguation4/5
Most tools have distinct purposes, but analyze_coverage and feedback_status both address coverage gaps, causing slight ambiguity. Overall, the majority are clearly distinguishable.
Naming Consistency5/5
Nearly all tools follow a consistent verb_noun snake_case pattern (e.g., generate_tests, run_mutation_tests). Only health deviates slightly but is still clear.
Tool Count5/5
With 14 tools, the surface covers all necessary operations for a test framework server without being excessive. Each tool serves a well-defined role.
Completeness5/5
The tool set covers the entire workflow from test generation, validation, registry, execution, mutation testing, reporting, and import. No obvious gaps for the intended domain.
Average 3.6/5 across 14 of 14 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on April 14, 2026
- 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.
Add a glama.json file to provide metadata about your server.
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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only states what the tool does without indicating side effects, permissions, or output characteristics. It does not say whether it modifies state or what 'gaps' means in terms of behavior.
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, front-loaded sentence that efficiently conveys the tool's purpose. There is no unnecessary repetition, and it is easy to read. However, it could be expanded slightly to improve completeness without sacrificing brevity.
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 and 4 parameters, the description is very brief. It does not explain what constitutes a 'gap', how coverage is measured, or the role of optional parameters like 'language' and 'source_code'. This under-specification leaves the agent unclear on how to invoke the tool effectively.
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 input schema has 100% description coverage, so parameters are documented in the schema. The description adds no additional semantics beyond the schema. Baseline score of 3 is appropriate.
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's purpose: analyzing test coverage and identifying gaps. The verb 'analyze' with the resource 'coverage' is specific. However, it does not explicitly distinguish itself from sibling tools like 'validate_test_contract' or 'build_traceability_matrix', which might also involve analysis.
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, prerequisites, or when alternatives are better suited. There is no mention of required input state or context. For a tool with multiple siblings, this omission reduces usability.
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 must disclose behaviors. It mentions 'Never blocks generation' which is a key behavioral trait, but omits details like failure modes, side effects, or required permissions. Minimal 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 with no unnecessary words. Front-loaded with the primary action and output. Every word 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?
The tool has 5 parameters and an output schema, but the description does not explain return values or error handling. For a test execution tool, details about partial failures or result structure would improve completeness.
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%, so the baseline is 3. The description adds no additional meaning beyond the parameter descriptions already present in the schema (e.g., language, framework, etc.). Hence no extra value.
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 action ('Execute test files') and the output ('structured results with CI annotations'). It does not explicitly differentiate from siblings like 'generate_tests', but the verb 'execute' makes its purpose distinct.
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 like 'analyze_coverage' or 'validate_test_contract'. The only usage hint is that execution is optional and non-blocking, which is context but not a comparison.
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 exist, so the description must compensate. However, it only states the detection function and reveals no behavioral traits such as execution context, latency, side effects, or security implications. The agent cannot assess safety or performance expectations.
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 with no wasted words. It is front-loaded with the primary action. While terse, it efficiently conveys the core purpose, earning a high score for conciseness despite lacking depth.
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 existence of an output schema, return values need not be explained. However, the description omits details like supported languages/frameworks, error handling, and the role of the file_path parameter. This incompleteness could lead to incorrect invocation.
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%, so the schema already documents both parameters. The description does not add extra meaning beyond what is in the schema. Baseline of 3 is appropriate as no additional clarification is needed.
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 detects programming language and test framework from source code, using a specific verb and resource. It distinguishes from sibling tools like generate_tests or validate_test_contract which have different purposes.
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 usage guidance is provided. The description does not indicate when to use this tool, when not to, or mention any alternative tools among the siblings. The agent receives no hints about prerequisites or scenarios for using this tool.
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. The description does not disclose behavioral traits such as read-only nature, side effects, authentication requirements, or limitations. It only states it queries the registry.
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, front-loaded sentence with no wasted words. It is concise, though perhaps overly brief for such a complex tool.
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 an output schema existing, the description lacks context about what the registry is, what the query returns, and how results are structured. Given six parameters and a rich sibling set, more detail is warranted.
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 each parameter having a description in the schema. The description adds no additional parameter information, 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 tool's function: 'Query the UTF persistent test registry.' It uses a specific verb ('Query') and resource ('test registry'), and distinguishes from sibling tools like generate_tests or run_tests.
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 (e.g., analyze_coverage, generate_tests). No context about prerequisites or scenario appropriateness.
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 mentions analyzing and suggesting but does not disclose whether the tool is deterministic, what side effects (if any) occur, required permissions, or the nature of suggestions (list of types, ranked scores, etc.). The term 'suggest' is vague.
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, concise sentence that effectively communicates the core function. However, it lacks structure (e.g., no additional context or examples) and could benefit from being slightly more detailed without becoming verbose.
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 (not shown) and fully described parameters, the description does not specify what form the suggestions take (e.g., a list of test type names, a prioritized output). It omits important contextual details that would help an agent use the tool effectively, such as whether both parameters are required or how they interact.
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 both parameters ('source_code', 'requirements_text') having clear descriptions. The tool description adds 'analyze code/requirements' which aligns with the parameters but does not provide additional clarity beyond the schema. 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 tool's purpose: analyzing code and/or requirements to suggest test types. This verb+resource structure effectively distinguishes it from sibling tools like 'generate_tests' (which generates actual tests) and 'analyze_coverage' (which analyzes code coverage).
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 is provided on when to use this tool versus its alternatives. The description lacks any 'when to use' or 'when not to use' context, which is particularly problematic given the number of closely related sibling tools (e.g., 'run_tests', 'generate_tests', 'validate_test_contract').
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 full burden but only says 'Build a ... matrix', omitting behavioral details such as whether the matrix is returned, saved, or has side effects. No mention of required permissions, rate limits, or idempotency.
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, concise and front-loaded. However, it could be slightly more informative without significant expansion.
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 that an output schema exists, return value explanation is less critical. However, the description lacks context about prerequisites (e.g., format of test objects), error handling, or performance implications for large arrays.
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%, so baseline is 3. The description does not add extra meaning beyond what the schema already provides for each 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 the verb 'Build' and the specific resource 'requirements-to-tests traceability matrix', distinguishing it from sibling tools like analyze_coverage and generate_tests.
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 such as analyze_coverage or generate_tests. The description does not mention prerequisites, typical use cases, or exclusions.
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?
The description discloses key behaviors: automatic language/framework detection, test generation for multiple path types, contract validation, traceability matrix building, and coverage gap identification. Since no annotations are provided, the description carries the full burden. However, it omits the side effect of storing results in <project_dir>/.utf/, which is mentioned only in the parameter description.
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 6 bullet points and a clear first sentence. Every sentence adds value without redundancy. Slightly more structure (e.g., separating mandatory/optional steps) could improve it, but it is efficiently written.
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 adequately explains the tool's automatic capabilities given the 7-parameter schema and existence of an output schema. However, it fails to mention the persistent storage side effect (registry and reports written to disk), which is important for understanding the tool's full impact. Output schema reduces the need to describe return values, but behavioral completeness is slightly lacking.
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 all parameters well-documented. The description does not add parameter-specific meaning beyond the schema; it provides high-level context (e.g., automatic detection) but does not elaborate on individual parameters. 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 'Generate a complete test suite satisfying the 8-section test contract.' The verb is specific (Generate), the resource is a test suite with a defined scope (8-section contract). It distinguishes itself from sibling tools like 'validate_test_contract' and 'analyze_coverage' by focusing on generation.
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 alternatives. The description lacks 'when to use', 'when not to use', or mentions of prerequisites. Siblings like 'validate_test_contract' or 'analyze_coverage' exist, but the description does not guide the agent on choosing this tool over them.
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 indicates a read operation ('Get') but does not explicitly state that it is non-destructive, has no side effects, or any authentication/rate-limit constraints. The description is minimal but not misleading.
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 a single concise sentence that front-loads the main purpose and key outputs. Every part is meaningful and no redundant information.
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 6 optional parameters and an output schema (not shown), the description covers the essential facets. It does not detail return format, but the output schema presumably handles that. Slight gap: no mention of defaults or how project context is resolved, but overall adequate for a status-query 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?
All 6 parameters have schema descriptions (100% coverage), so the baseline is 3. The tool description adds context by mapping components like 'gap analysis' to check_gaps and 'trend' to compute_trend, but this is already implied by parameter names and schema descriptions. No significant extra 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 verb 'Get', the resource 'UTF feedback loop status', and enumerates the components: gap analysis, coverage health, trend, and delta requirements. This is specific and distinguishes it from sibling tools like analyze_coverage or generate_report.
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 its use for obtaining a summary status of the feedback loop, but it lacks explicit guidance on when to use this tool versus alternatives (e.g., query_registry for raw data, analyze_coverage for deep dive). No when-not-to-use or alternative references are provided.
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 the full burden. It mentions auto-detection of adapters and return of coverage metrics, but lacks details on side effects, permissions, or failure behavior. The timeout parameter is in the schema, not the description.
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 that front-load the purpose followed by supported tools. No wasted words, clear and efficient structure.
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 7 parameters (3 required) and existence of an output schema, the description covers the main purpose and tool support. It could elaborate on parameter roles like block_below and minimum_score, but the output schema compensates for return values.
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%, so the baseline is 3. The description does not add meaning beyond the schema; it only mentions supported tools and auto-detection, not parameter details.
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 'run' and the resource 'mutation testing on a source+test file pair'. It distinguishes from sibling tools like 'run_tests' and 'analyze_coverage' by focusing specifically on mutation testing and listing supported adapters.
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 for mutation testing but does not explicitly state when to use this tool versus alternatives. No when-not or alternative tool names are provided beyond the sibling list.
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 that the tool reads from the registry, builds the report, and returns paths and summary. It does not mention any destructive actions (likely none), and the read-only nature is implied. However, it could explicitly state that the tool does not modify any data.
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 two sentences, front-loading the purpose and then summarizing the process and output. It is concise, but the inclusion of 'Tool #12' is unnecessary and adds no value for the AI agent. Otherwise, every sentence 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 complexity and the presence of an output schema, the description is largely complete. It explains that input comes from the registry and output includes report paths and a summary. However, it does not mention prerequisites (e.g., that test records must exist) or the structure of the summary.
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%, so the baseline is 3. The description does not add additional meaning beyond the schema; it only reinforces that formats are requested. The description's mention of 'requested formats' aligns with the schema but does not provide extra context.
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 generates an '8-section contract compliance report', specifies it reads from the registry and produces multiple formats, and returns paths and summary. It is a specific verb+resource that distinguishes it from siblings like 'generate_tests' or 'validate_test_contract'.
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 the tool is used after test execution to build a compliance report, but it does not explicitly state when to use it versus alternatives (e.g., after tests are run, or when a comprehensive compliance report is needed). No when-not-to-use or alternative tooling guidance is provided.
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 must carry the full burden. It describes the core action and references the registry but does not disclose behavioral details like overwrite behavior, authorization needs, or error handling.
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 two sentences: the first stating the action and the second providing usage context. It is efficient, though the term 'UTF registry' may be slightly obscure without context.
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 rich input schema (100% coverage) and the presence of an output schema, the description adequately covers the tool's purpose, prerequisites, and downstream integration with generate_report. Minor gaps like behavioral specifics are acceptable.
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%, so the description does not need to add extra parameter meaning. It does not elaborate beyond what the schema already provides, achieving the baseline score.
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 'Import' and the resource 'JUnit XML execution results into the UTF registry'. It also distinguishes from siblings like run_tests and generate_report by specifying that tests have already been 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 gives explicit guidance on when to use: 'when you have already run tests... and want to register the results so that generate_report shows real pass/fail rates'. It implies but does not explicitly state when not to use or name alternatives.
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. It describes the validation purpose and required sections but does not disclose details about side effects, permissions, or return format. The output schema exists but is not referenced.
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: first defines the action, second provides usage context and lists required sections. No filler, 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?
Complete enough for a validation tool with an output schema. It explains what the tool does and when to use it, though it could clarify which parameter to choose for different inputs.
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%, so baseline is 3. The tool description does not add meaning beyond the schema descriptions, which already explain each parameter's role.
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 tool validates a test or test suite against an 8-section contract. The verb 'validate' and resource 'test or test suite' are specific and distinct from sibling tools like generate_tests or analyze_coverage.
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?
Explicitly says 'Use this to check any existing test — generated or hand-written — for compliance,' indicating when to use. However, it does not specify when not to use or mention 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 explains the tool reads test files, extracts comment blocks, and upserts rows into the registry, which is transparent. However, it does not disclose error handling, permissions, or output schema details.
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 a clear first line, an explanation paragraph, and a numbered workflow list. Every sentence is informative with no wasted words.
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 complexity (6 params, output schema, many siblings), the description effectively covers the tool's role in the workflow. It omits edge cases or failure modes but is sufficient for typical use.
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%, so the baseline is 3. The description adds workflow context but does not elaborate on parameter usage beyond what the schema already provides.
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 parses test files and registers per-method contract records. It distinguishes itself by placing it as a specific step in the UTF workflow, differentiating from siblings like generate_tests and generate_report.
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 lists the 3-phase workflow with steps ①-⑦, positioning register_contracts as Step ③. It also states that without this step, generate_report has no contract detail cards, providing clear when-to-use context.
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?
Fully discloses return fields and implies read-only, safe operation. No annotations provided, but description compensates completely.
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?
Short, front-loaded with purpose and use cases, then return fields. Every sentence is informative and concise.
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 for a parameterless health check: defines purpose, usage context, and output. No gaps given the tool's simplicity.
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?
No parameters; schema coverage 100% vacuously. Description adds value by detailing return values beyond 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?
Clearly states it is a health check returning status, version, uptime, and tool count. Distinct from siblings like generate_tests or run_tests.
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 lists suitable use cases: Docker HEALTHCHECK, Kubernetes probes, and basic availability verification, providing clear guidance on when to use.
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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