mk-qa-master

by kao273183

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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

Latest release: v1.4.0
Disambiguation4/5
Most tools have distinct purposes (e.g., analyze_url vs analyze_screen vs analyze_stream for different platforms), and descriptions are thorough. However, some overlap exists between auto_generate_tests and the combination of analyze_url + generate_test, and the sheer number of tools (22) increases potential for misselection.
Naming Consistency4/5
Tool names follow a consistent snake_case and verb_noun pattern (analyze_url, generate_test, run_tests). Minor deviations include auto_generate_tests (two verbs) and a few longer names like get_failure_details, but overall the pattern is predictable.
Tool Count3/5
22 tools is on the heavy side, but the server covers a broad domain (QA for web, mobile, stream, API security). Some tools could be merged (e.g., auto_generate_tests vs analyze_url+generate_test), making the surface feel slightly crowded for the scope.
Completeness4/5
The tool set covers the QA lifecycle well: analysis (analyze_url, analyze_screen, analyze_stream), test generation (generate_test, auto_generate_tests), execution (run_tests, run_failed), reporting (get_test_report, generate_html_report), and optimization (get_optimization_plan). Minor gaps include lack of direct test editing or test data management tools.
Average 4.5/5 across 22 of 22 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- 3 of 3 community issues answered or closed in the last 6 months
- 26 commits in the last 12 weeks
- Last stable release on June 4, 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.
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

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 discloses key behaviors: base64 embedding, no external dependencies, default output path, and implementation location. However, it does not mention side effects like file overwriting, potential size issues, or whether it triggers a test run.
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 paragraph that front-loads the main purpose, then details features and outputs. It is relatively concise for the amount of information provided, though could be slightly more structured.
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 tool's complexity (generating a rich HTML report) and no output schema, the description lacks completeness. It does not specify what the tool returns (e.g., success status, file path), when it fails, or how it handles missing test results. The usage context is clear but insufficient.
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 1 parameter with 100% coverage (its description is provided). The tool description adds minimal extra meaning beyond the schema, just reiterating the default output path. No additional constraints or usage details are given.
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 renders results from the last run_tests into a self-contained HTML report, listing specific features. It distinguishes itself from siblings by its self-contained nature, but does not explicitly compare to get_test_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 usage after running tests ('最近一次 run_tests 的結果'), but does not explicitly state when to use this tool versus alternatives like get_test_report, 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It details the internal sequence (analyze_url, then generate_test per module), side effects (writing files to PROJECT_ROOT/tests/), behavior of tests_per_module (up to 10 may produce garbage), and the plan_id feature attaching evidence rows. It does not cover error handling for analyze_url failures, but the plan bookend section acknowledges generation failures.
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 lengthy and contains non-English text (Chinese), which may reduce clarity. However, it is front-loaded with the core action and each subsequent sentence adds useful detail. It could be more concise by separating the plan bookend section more elegantly.
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 no output schema, the description explains the return value (list of file paths and test counts), and the enhanced response with plan_id. It covers the workflow, parameters, and limitations. It is largely complete for a composite tool with 5 parameters, though the exact return structure without plan_id could be more explicit.
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 100%, baseline 3. The description adds value beyond schema for multiple parameters: tests_per_module warns about garbage at high values, auth_cookie provides practical source instructions, and plan_id explains the plan_verification envelope and evidence row structure.
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 automates the process of analyzing a URL and generating tests for each detected module, writing pytest skeletons to a directory. It differentiates itself from siblings by being a one-click automation of manual sequential steps, as explicitly noted.
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 specifies the tool is suitable for rapid coverage scenarios where the user provides a URL and expects automated test generation. It contrasts with manual execution of analyze_url and generate_test, and mentions the optional plan_id integration for QA plan workflows. However, it does not explicitly state when not to use it or provide alternatives for fine-grained control.
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?
With no annotations provided, the description fully carries the burden. It details the analysis logic (flake scoring, broken test detection, pattern mining) and discloses that results are written to a file (optimization-plan.md), which is a write operation with potential persistence effects.
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 dense paragraph covering complex logic. While each sentence is necessary, it could be better structured (e.g., bullet points) to improve readability for an AI agent.
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?
The description thoroughly explains the three analysis layers and their logic. However, it only states that the tool returns 'structured JSON' without detailing the return format, and it lacks information about error handling or prerequisites, leaving minor 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 parameter descriptions already explaining history_limit and telemetry_limit. The description does not add extra meaning beyond what the schema provides, so the baseline score of 3 applies.
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 produces a three-layer analysis (test suite quality, MCP usage patterns, AI test generation effectiveness) and writes results to a file. This distinguishes it from sibling tools like get_test_history or analyze_url, which have narrower scopes.
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 that the tool is automatically triggered after run_tests, so it is used to 'read results in real-time.' This provides clear context, though it does not explicitly list when to avoid using it or name 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?
The description discloses that the tool reads an environment variable and returns configuration data, implying a read-only operation with no side effects. Without annotations, it provides sufficient behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph that covers all necessary points: purpose, usage guidance, and return format. It is clear and informative, though slightly verbose.
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 no parameters and no output schema, the description provides a complete overview including return shape and context. It could mention error cases (e.g., missing env variable) but is otherwise thorough.
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 no parameters, so schema description coverage is 100%. The description does not add parameter meaning beyond the schema, which is expected. Baseline score of 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 returns the active runner from the QA_RUNNER environment variable along with the list of all built-in runners. It distinguishes itself from sibling tools by focusing on runner configuration info.
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 recommends calling this tool first in each session and explains how the returned information is used to decide which test framework to generate, whether to use headed browser, and to verify environment setup.
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 that the tool reads snapshot files (non-destructive) and returns summary data. While no annotations are provided, the behavioral traits are adequately covered, though it does not mention any potential performance implications 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 concise enough, covering the core functionality in a few sentences with key information front-loaded. It could be slightly more compact, but it is not verbose.
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 explains the tool's purpose, return fields, use cases, and parameter. Given the simple input schema and no output schema, the description provides sufficient context for correct 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?
Schema coverage is 100% and the parameter 'limit' is well-documented in the schema with default, range, and use-case recommendation. The description adds minimal additional meaning, 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 traverses test history snapshots and returns per-run summaries with specific fields (timestamp, total, passed, etc.). It also lists concrete use cases (flake analysis, speed degradation, coverage trends), making the purpose unambiguous.
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 states when to use the tool (e.g., flake analysis, speed degradation) and directs users to a sibling (get_optimization_plan) for actionable suggestions, providing clear guidance on tool 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, description discloses idempotency, overwrite behavior, and that overwrite=true causes content loss. Adequately informs about file creation and 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise, front-loaded with purpose, and covers all essential aspects. Slightly dense but 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?
Given simple parameter and no output schema, description is complete: explains what file is created, sections included, idempotency, recommended usage, and integration with other tools.
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% (overwrite param has description). The description adds value by explaining the backup recommendation and reinforcing the overwrite behavior 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 creates a starter template 'qa-knowledge.md' with specific sections (Business Rules, Historical Bugs, etc.). It distinguishes from siblings by focusing on initialization of a knowledge base, not analysis or test generation.
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?
Provides guidance on idempotency, recommends calling on first run, and explains downstream use (read by get_qa_context, used in generate_test). Lacks explicit when-not-to-use, but context is clear.
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 fully carries the burden. It details the underlying commands per framework (e.g., pytest --collect-only) and the return format (list of nodeid/filename). It could mention that the tool is read-only and safe to run, but overall it is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: first sentence lists mechanisms for each framework, second sentence describes the return format, and third provides usage guidance. No extraneous 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 no output schema, the description explains the return format (list of nodeid/filename). It is complete enough for a listing tool, though it could mention potential performance impacts for large projects. Overall, it provides sufficient context.
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, and schema coverage is 100% (trivially). The description adds no parameter-level details because none are needed, but it does explain the tool's behavior without relying on params. Baseline 4 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 it lists all executable tests using native collection mechanisms for multiple frameworks (pytest, Jest, Cypress, Go, Maestro). It distinguishes itself from sibling tools like run_tests (execution) and generate_test (creation) by specifying its purpose.
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 explicit usage scenarios: before run_tests to confirm coverage and before generate_test to avoid duplicates. It gives clear context but does not explicitly mention when not to use the 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?
In the absence of annotations, the description thoroughly discloses the tool's behavior: it dumps the hierarchy, classifies modules, applies noise filtering, and requires specific operational conditions. It also explains the launching behavior with clearState: false. This provides good transparency beyond the schema.
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 paragraph but packs substantial information efficiently. It is front-loaded with the main purpose and then details. While slightly dense, it avoids unnecessary verbosity and is structured logically.
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 and the lack of an output schema, the description is highly complete. It explains what the tool returns (hierarchy, classified modules, candidate_tcs), lists prerequisites, and covers optional behaviors comprehensively. No significant gaps remain.
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?
The input schema has 100% coverage with descriptions for all three parameters. The description adds significant context: app_id is used for launching or labeling, launch_app details the exact behavior (clearState: false), and timeout_ms adjusts for BlueStacks. This goes beyond what the schema 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 identifies the tool as a mobile version of analyze_url, specifying that it dumps the view tree of the current foreground app on a mobile device, classifies UI elements into forms, CTAs, and tab bars, and filters noise. It differentiates from the sibling analyze_url by explicitly stating 'Mobile 版的 analyze_url'.
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 clear context for when to use the tool, including prerequisites (Maestro CLI installed, device booted, app in foreground) and options to launch the app. It does not explicitly mention when not to use it or list alternatives, but the context is sufficient for selecting the 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?
With no annotations provided, the description carries full burden. It discloses that the tool requires consent, only operates on the active page, and never clicks. It details return structure, error shapes, and scope (v0.7.0 reCAPTCHA v2 only). This is thorough and transparent.
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 moderately long but well-structured. It front-loads the main purpose, then provides details on consent, return, errors, and scope. Every sentence adds value, though it could be slightly more compact.
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 lack of output schema, the description compensates by detailing the return structure and error shapes. It covers consent, scope, and pairing. Minor omission: not fully describing challenge_text or fingerprint format, but sufficient for the tool's task.
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% and parameters are well-documented. The description adds context: page_id is ignored in current version, and selector has default auto-detection logic. This provides meaningful guidance 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 detects a reCAPTCHA v2 image-grid challenge, screenshots it, and returns tile metadata. It distinguishes itself from the sibling solve_visual_challenge by noting that this tool never clicks anything, providing a clear purpose.
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 (detecting challenges), mentions the need for consent, and pairs it with solve_visual_challenge. However, it does not explicitly state when not to use it (e.g., if no challenge present), though error shapes hint at that. It lacks explicit alternatives but context is clear.
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 it reads a file from a previous run, is non-destructive, and returns a summary. It also explains the conditional edge_metrics block. However, it does not explicitly state idempotency or potential side effects (though none likely).
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 detailed and contains important usage guidance. However, it includes version-specific details that could be separated (v1.3.0+ edge AI runner info). It is front-loaded with the core functionality.
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 no output schema, the description thoroughly explains the return shape (fields total, passed, failed, skipped, flaky_in_run, duration) and the error case. It also covers the edge metrics block for Edge AI runner. The tool has no parameters, so this is sufficient for complete usage.
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 4 applies. The description correctly adds no parameter information since none exist.
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 reads the previous test report and returns a lightweight summary, specifying exact fields (total, passed, failed, skipped, flaky_in_run, duration). It distinguishes itself from run_tests (cheaper) and get_failure_details (for detailed failure info).
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 to use (between continuous operations, checking status cheaply) and when not (if no previous run, returns error). It also provides alternative actions: if failed > 0, use get_failure_details. Mentions edge AI runner specifics and leads to get_optimization_plan.
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?
With no annotations, the description fully discloses behavioral traits: cache TTL of 30 minutes, LRU bound of 50 plans, disk persistence behavior, error shapes, and version history. This provides comprehensive transparency beyond what structured fields could offer.
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 detailed but well-structured, starting with the primary purpose, then usage pattern, followed by technical details. It could be slightly more concise, but 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 description fully covers all aspects: purpose, usage, parameters, return value, error shapes, persistence, and expiry. It also links to the sibling tool verify_plan, making it complete for a tool of this complexity.
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 extra meaning: auto-assignment of IDs for critical_points, defaulting verification_hint to description, and the purpose of the 'kind' enum. This enhances understanding beyond the schema alone.
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: 'Store a critical-points checklist before acting on a QA task.' It specifies the return of a plan_id and outlines the complementary workflow with verify_plan, distinguishing it from sibling tools like verify_plan.
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 (before acting on a QA task) and how it pairs with verify_plan. It does not explicitly list exclusions or alternatives, but the context is clear enough for an agent to understand proper usage.
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?
With no annotations provided, the description fully covers behavioral traits: it discloses that mass_assignment mutates server state, describes error shapes, explains severity_threshold behavior, and details return structure including plan_verification.
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 front-loaded with version and core purpose. It is fairly long but each sentence adds value. Minor improvements could be made to structure with bullet points, but overall it is appropriately sized.
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 (7 parameters, no output schema), the description provides comprehensive context: return shape, error shapes, authentication details, plan verification, version history. It leaves no critical 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, but the description adds significant meaning beyond the schema, such as explaining how auth config enables different rules, default categories, and the effect of plan_id. This extra context justifies a score of 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 is an OWASP API Security Top 10 rule-based scanner that loads an OpenAPI spec and runs specific rules. It distinguishes itself from sibling tools like run_tests and qa_plan by focusing on security scanning.
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 for when to use the tool and prerequisites (consent, authorized domains). It explains opt-in for mass_assignment. However, it does not explicitly state when not to use it versus alternatives like qa_plan or verify_plan.
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?
Even without annotations, the description is highly transparent. It details the safety latch (confirm), dynamic-replace mode, telemetry logging constraints (only boolean outcome, no screenshots/challenge text/tile selection persisted), and plan bookend behavior. No contradictions 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections for core behavior, dynamic-replace mode, and plan bookend. It is front-loaded with the main purpose. Some minor redundancy (e.g., explaining telemetry twice) but overall efficient for its 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 no output schema, the description fully enumerates all possible return statuses and their fields. It covers edge cases (e.g., consent_required, confirm_required) and explains dynamic mode continuation. Highly complete for a complex CAPTCHA-solving 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?
While input schema already describes each parameter, the description adds valuable context: challenge_id expiry (5 min), tile index mapping for grids, confirm as safety latch, and plan_id optionality with evidence summary. Schema coverage is 100%, so baseline is 3; description adds enough to raise to 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 verb and resource: 'Apply the AI client's tile selection, execute the click chain, click Verify, wait for the reCAPTCHA token, and return the outcome.' It explicitly pairs with inspect_visual_challenge and differentiates by being the solve step, distinguishing it from siblings.
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 explicit pairing with inspect_visual_challenge and requires challenge_id from that call. It explains dynamic-replace mode and when to pass empty selected_tile_indices to finalize. However, it does not explicitly state alternatives 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.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that description becomes docstring and HTML case name, url+module prefill selectors, filename restrictions (no absolute path or '..'), and business_context usage. No annotations provided, so description fully shoulders the burden.
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?
Front-loaded with purpose and workflow, then details. Slightly lengthy but all sentences add value; no redundancy.
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?
Covers purpose, usage, parameter details, and behavioral notes for a 5-parameter tool without output schema. Could explicitly state that it creates a file, but implied.
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 description adds valuable usage context for description (from candidate_tc) and business_context (call get_qa_context), going beyond 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?
The description states it generates pytest-playwright test skeletons and recommends a workflow with analyze_url, clearly distinguishing it from auto_generate_tests for whole URLs.
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 describes when to use (after analyze_url, per candidate TC) and when not (for whole URL, use auto_generate_tests), with a clear workflow recommendation.
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?
With no annotations provided, the description fully covers behavioral traits: fallback to built-in knowledge, case-insensitive partial section matching, and the output structure (whole file with section list when no section specified). 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 reasonably concise in Chinese (4-5 sentences), front-loading the core purpose and usage. While every sentence adds value, it could be slightly more compact without losing meaning.
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 one optional parameter, no output schema, and no annotations, the description is fully adequate. It explains fallback, usage, and output, making the tool's behavior predictable for an AI agent.
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?
The single optional parameter 'section' has 100% schema description coverage. The description adds significant value by explaining matching behavior (case-insensitive, partial match) and the effect of omission vs. specification, which goes 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 reads a project-specific qa-knowledge.md file, which distinguishes it from sibling tools like init_qa_knowledge (which creates the file). The verb '讀取' (read) and specific resource are 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 outlines the usage pattern: call this tool first to get knowledge, then pass sections to generate_test. It also notes fallback behavior when the file doesn’t exist. While it doesn’t explicitly state when not to use it or list alternatives, the guidance is clear for the intended workflow.
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?
With no annotations, the description fully discloses behavioral traits: matching rule (case-insensitive substring), flattening of evidence, auto_discover behavior (best-effort, silent skip), status semantics, persistence cache, and error shapes. Comprehensive.
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 layered detail: main purpose first, then matching rule, then version updates. Every sentence provides necessary information without verbosity.
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?
Completely describes return shape with field details, error shapes, and behavioral nuances. Without an output schema, the description fully compensates.
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%, and the description adds significant context: matching rule for evidence, auto_discover details, report_path fallback logic. Adds value beyond 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?
The description clearly states it verifies a plan's critical points against evidence, names the sibling `qa_plan` for pairing, and specifies the return type. It distinguishes itself from other tools by focusing on verification of plans.
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 mandates calling with `plan_id` from a prior `qa_plan` call and describes auto_discover usage. Does not explicitly state when not to use, but the pairing guidance is clear.
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?
With no annotations, the description fully discloses behavior: vendor host blacklist, opt-out via env var, extra requirement, error returns, non-fatal missing annotations, and return structures for success and rejection.
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 front-loaded with version and summary, then logically organizes details about return structure, restrictions, requirements, and error returns. Every sentence adds value 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 two parameters and no output schema, the description comprehensively covers input semantics, return structures for all outcomes (success, error types), and edge cases (missing extras, vendor host blacklist, malformed annotations).
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 already has descriptions, but the description adds significant context: for rtsp_url it explains file path likely served via mediamtx, for annotations_path it details format and fallback behavior. This goes 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: probing an RTSP stream or file path for geometry and candidate_tcs, with explicit comparison to siblings analyze_url and analyze_screen. The verb 'probes' and output listing make it 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?
Provides use context (Edge AI), requirements (extras), and restrictions (vendor host blacklist with opt-out). It does not explicitly state when not to use compared to siblings, but the comparison to analyze_url and analyze_screen implies alternatives.
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?
With no annotations, the description fully discloses behavior: page load strategy (DOMContentLoaded + 5s networkidle), DOM probe extraction, XHR recording, layout overflow scan, and return structure. Edge cases (unreachable URL, no forms, login-walled, SPA) are detailed.
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 (behavior, usage, edge cases, plan bookend) and front-loaded with purpose. While comprehensive, it is slightly verbose but still efficient for the 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?
Despite no output schema, the description fully explains the return structure (url, page_title, modules, api_endpoints, layout_warnings) and error case. It covers all relevant context for a tool with 4 parameters and no 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?
Schema coverage is 100%, so baseline is 3. The description adds context beyond schema: timeout behavior (extra 5s wait), auth_cookie format, and plan_id purpose. This adds meaningful 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 the tool's purpose: probing a live web page with headless Chromium to return testable modules and API endpoints. It uses specific verbs and distinguishes itself from sibling tools like analyze_screen.
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 includes explicit 'When to use' and 'When NOT to use' sections, listing specific scenarios and alternative tools (e.g., mobile apps → analyze_screen, immediate generation → auto_generate_tests). Edge cases are also covered.
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?
Despite no annotations, the description fully discloses the tool's behavior: it runs only last-failed tests, explains framework-specific mechanisms (pytest --lf, Jest --onlyFailures, etc.), and states the return shape matches run_tests. 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 moderately long but well-structured: it starts with the core purpose, then lists framework-specific details, and finishes with return shape and prerequisites. Every sentence adds value, though slightly verbose; could be trimmed slightly without loss.
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 (supporting multiple test frameworks) and lack of output schema, the description is comprehensive. It covers usage context, prerequisites, behavior per framework, and return shape, making the tool usable without additional context.
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, and schema coverage is 100% vacuously. Following the guideline '0 params = baseline 4', the score is set to 4. The description does not add parameter information because none exists, but it compensates by explaining the tool's behavior and prerequisites.
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 reruns only the previously failed tests, using specific verbs like '只重跑' and contrasting with the full suite. It differentiates from sibling tool 'run_tests' by specifying the target as only failed 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 says when to use ('適合修完一個 bug 後驗證迭代') and notes the prerequisite that a prior run_tests must have been executed. It also mentions the tool is faster than running the full suite, guiding the agent to use it for efficient iteration.
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?
No annotations are provided, so the description carries the full burden. It details the invocation (pytest with screenshot/tracing/video flags, jest/cypress/maestro commands), output files (report.json, JUnit XML), side effects (snapshot to history, auto-trigger optimizer.write_plan), Maestro auto-retries, and edge cases (no match, timeout, security guardrail). This is comprehensive.
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 structured with labeled sections (Behavior, When to use, Edge cases, Plan bookend) and front-loaded with the core purpose. While it is somewhat lengthy, every section earns its place by providing necessary detail. Minor redundancy in the 'When to use' list but overall 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?
Given no annotations or output schema, the description covers all critical aspects: behavior, return shape (exit_code, stdout_tail, etc.), side effects, edge cases, and the plan bookend feature. It references sibling tools and explains when to use alternatives. The description is complete for a complex 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?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining how 'filter' works per runner (pytest -k, jest -t, etc.), that 'headed' is only for pytest-playwright, 'browser' requires pre-installation, and 'plan_id' ties to qa_plan with response shape change. It enriches the schema without redundancy.
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 executes the test suite and produces a structured report. It identifies itself as the 'single most-called tool' for running tests and lists triggers like 'run/test/check/驗證/執行'. It distinguishes the primary action (execute) from related tools like get_test_report and run_failed.
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 states when to use (after writing a new test, smoke before release, when user prompt contains run/test verbs) and when NOT to use (inspect results without re-running → get_test_report, re-run only failures → run_failed, list tests → list_tests). This provides clear alternatives and 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?
No annotations exist, so the description carries full burden. It details opening a real Chromium window, transcribing actions, saving output, security guards on output path, Chromium installation failures, and fallback hints for non-Playwright runners.
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 headings and bullet points. Every section adds value, though it is somewhat lengthy. The purpose is front-loaded, and the structure aids scanning.
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 (multiple runners, security concerns, no output schema), the description covers return value, all relevant behaviors, and edge cases comprehensively.
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%, and the description adds meaningful context beyond parameter names: url is the starting page, output is a relative path with security constraints (no `..`, no absolute).
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 opens with a clear verb+resource: 'Launch interactive test recording for the active runner.' It distinguishes from sibling tools like generate_test by framing this as a baseline-builder before refinement.
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 'When to use' and 'When NOT to use' sections list concrete scenarios and alternative tool names (e.g., generate_test, auto_generate_tests). The 'When NOT to use' section covers headless environments and mobile flows.
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?
No annotations provided, so description carries full burden. It discloses reading report.json, filtering by outcome, parsing behavior for pytest/Maestro, artifact resolution, error handling for missing files, and edge cases. Very transparent about behavior and limitations.
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 (Behavior, Use cases, Non-use cases, Edge cases) and front-loaded. However, it is somewhat lengthy and could be slightly more concise, though 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 complexity (multi-framework, multiple artifact types), no annotations, and no output schema, the description covers behavior, parameter details, return format, and edge cases comprehensively. It is complete for an agent to understand and use the tool effectively.
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?
Only one parameter (test_id) with 100% schema description coverage. The description adds meaning: 'optional, substring match, case-insensitive, omit for all failures, common usage pattern', which goes beyond the schema and helps the agent use it correctly.
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 extracts full root-cause-analysis materials for every failed test in the most recent run. It specifies the verb 'extract' and the resource 'full root-cause-analysis materials for failed tests', and differentiates from siblings like get_test_report and get_test_history.
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 provides when to use (after run_tests with failures, user asking about failures, filing bugs, comparing across runs) and when NOT to use (want summary only, no tests run, want passing tests). Also covers edge cases, giving comprehensive 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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