PAL MCP Server

by bigl34

Overview Schema Related Servers Score Discussions

Python

Hybrid

Server Quality Checklist

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

Latest release: v9.10.0
Disambiguation3/5
Many tools have overlapping purposes, particularly among analyze, codereview, debug, thinkdeep, and tracer, which all involve systematic analysis of code. Although each targets a specific aspect (architecture, security, debugging), the boundaries are fuzzy, and agents may struggle to select the appropriate tool for a given task.
Naming Consistency4/5
Tool names are uniformly lowercase concatenated words (e.g., apilookup, codereview, testgen). While some names are generic (chat, clink), there is a consistent pattern overall. However, the lack of a standard verb_noun prefix (like list_, create_) limits predictability.
Tool Count4/5
With 18 tools, the server is at the upper end of a reasonable scope. All tools seem to serve a distinct purpose, though the overlap among analysis tools suggests some consolidation could be beneficial. The count is appropriate for a comprehensive development assistant.
Completeness4/5
The tool set covers a wide range of development tasks: analysis, review, debugging, security, documentation, testing, planning, and consensus. However, it lacks tools for executing code, searching code, or direct editing, which are common in such assistants. These gaps are minor given the existing coverage.
Average 3.8/5 across 18 of 18 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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.
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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?
Annotations already declare readOnlyHint=true, so the read-only nature is clear. The description adds 'step-by-step' and 'expert validation' but does not disclose other behavioral traits (e.g., auth needs, side effects, or what happens during the review process). The annotation covers the key safety aspect.
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 at two sentences, front-loading the purpose. It avoids redundancy, but could be slightly more structured by mentioning the step process earlier.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 22 parameters and no output schema, the description should provide more context on how to use the multi-step review process (e.g., step lifecycle, continuation_id usage). It lacks sufficient guidance for such a complex tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, meaning all 22 parameters have individual descriptions in the schema. The description adds no additional parameter-level information. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it performs systematic, step-by-step code review covering quality, security, performance, and architecture. It distinguishes itself from siblings like 'secaudit' or 'debug' by emphasizing a structured process and expert validation, but does not explicitly differentiate from all siblings.
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 says 'Use for comprehensive analysis' but provides no explicit guidance on when to use this tool versus alternatives (e.g., secaudit for security, debug for debugging). No when-not-to-use or conditions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description claims the tool 'creates' test suites, implying write operations, while annotations set readOnlyHint=true. This contradiction severely misleads agents about behavior. Score 1 per rubric.
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?
Three sentences that are direct and front-loaded. Could be slightly more structured (e.g., bullet points) but contains no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 17 parameters and no output schema, the description is too brief. It does not explain the step-based workflow or how parameters like step_number, findings, or next_step_required fit into the process.
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 baseline 3 applies. Description adds general context about test generation but does not elaborate on individual parameters beyond what 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?
Clearly states it creates comprehensive test suites with edge case coverage for specific functions/classes/modules. Distinguishes from sibling tools like codereview or debug by focusing on 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Advises to be specific about scope and target particular components, but does not explicitly compare with alternatives like docgen or refactor. Lacks when-not or alternative tool guidance.
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?
Annotations declare readOnlyHint=true, and the description aligns with analysis being read-only. However, the description does not disclose the multi-step workflow (step_number, total_steps), the use of continuation_id for multi-turn conversations, or the model selection behavior, which are critical behavioral traits beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, concise and front-loaded with purpose. No unnecessary repetition, though it could benefit from slightly more detail on workflow without being 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?
Given the tool's complexity (18 parameters, 6 required, multi-step workflow, no output schema), the description is too sparse. It omits the iterative step process, continuation_id usage, and how to interpret findings, leaving the AI agent underinformed.
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 alone fully documents parameters. The description adds no additional parameter-specific meaning, meeting the baseline expectation but not exceeding it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it performs comprehensive code analysis for architecture, performance, etc., and uses verb 'analyze' with specific domains. However, it does not explicitly differentiate from sibling tools like codereview or secaudit, which also analyze code.
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 says 'Use for architecture, performance, maintainability, and pattern analysis' but gives no guidance on when not to use it or alternatives. It lacks explicit when-not or comparisons to siblings.
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?
Annotations indicate read-only is false, but the description adds little beyond that; it mentions pre-applying conversation context but omits side effects, auth needs, or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, well-structured sentence with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 7 parameters, no output schema, and complex interactions (multi-CLI, role presets), the description is too terse and fails to explain response format or error conditions.
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 description adds minimal extra meaning; 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 it links a request to an external AI CLI to reuse capabilities, distinguishing it from direct action siblings like analyze or chat.
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?
It implies usage for integrating external CLI capabilities into workflows but lacks explicit when-not-to-use or alternative tool guidance.
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?
The description lacks disclosure of behavioral traits beyond the annotation 'readOnlyHint: false'. It does not specify side effects, state changes, or prerequisites for using this tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise, consisting of two sentences that effectively convey the tool's purpose and typical usage without unnecessary detail.
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 covers the purpose and typical use cases but omits details about the return format and conversation mechanics (e.g., continuation_id usage), which are only partially addressed in the schema.
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 each parameter has a clear description. The tool description adds general context but does not significantly enhance parameter understanding 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 states the tool is for 'general chat and collaborative thinking' for brainstorming, discussions, and exploring ideas. It differentiates from sibling tools like 'codereview' or 'debug' by being broad and open-ended.
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 lists use cases like 'ideas, validations, questions, and thoughtful explanations' but does not explicitly state when to avoid this tool or direct users to specific siblings for specialized tasks.
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?
Description says 'builds plans' implying modification, but annotation readOnlyHint=true suggests no side effects. This contradiction reduces transparency. Lacks details on plan storage, duration, or output format.
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?
Two sentences, front-loaded with purpose and capabilities. Efficient but could be structured with bullet points for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 13 parameters and no output schema, the description lacks sufficient guidance on how to use the tool effectively, such as step flow or interaction model.
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 covers 100% of parameters with descriptions, so baseline is 3. Tool description briefly mentions revision and branching, but does not add significant 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?
Tool name 'planner' and description clearly state it breaks down complex tasks for planning, system design, etc., distinguishing it from sibling tools like analyze, chat, or testgen.
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 recommends use for complex project planning, system design, migration strategies, and architectural decisions. Does not explicitly state when to avoid or name alternatives, but context signals and sibling list imply other tools for simpler tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, confirming no destructive side effects. The description adds behavioral context by detailing the two trace modes ('precision' and 'dependencies'), which is beyond what annotations convey. There is no contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences: first defines the core action, second lists use cases, third specifies modes. Every sentence adds value, and the most important information is front-loaded.
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 the tool having 15 parameters (8 required) and no output schema, the description remains very brief. It does not provide a narrative on how to use the tool effectively, nor explain the relationship between parameters or the expected workflow, leaving the agent to rely entirely on the schema.
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 elaborate on any parameters beyond what the schema already provides, but this is acceptable given the high coverage.
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 performs 'systematic code tracing' and specifies two modes: execution flow and dependency mapping. It lists use cases like method execution analysis and call chain tracing, but does not differentiate from sibling tools like 'analyze' or 'debug', which could overlap in function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit use cases such as 'method execution analysis, call chain tracing, dependency mapping', but does not offer when-not-to-use guidance or compare against alternative sibling tools. The usage context is implied rather than explicit.
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 consistently aligns with the readOnlyHint annotation, emphasizing validation and analysis. It adds value by explaining the structured multi-step process, use of expert models, and distinction between external and internal validation. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of three succinct sentences, immediately stating the core purpose, use cases, and guidance. No unnecessary wording; every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (24 parameters, no output schema), the description provides only high-level context. It omits details on the multi-step validation process and parameter interdependencies, which are partially covered in the schema but could benefit from description-level hints.
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 3 is appropriate. The description does not elaborate on parameter usage beyond what the schema provides, but the tool's high-level purpose and workflow context are clear.
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 validates git changes before committing, with specific use cases for multi-repository validation, security review, and change impact assessment. It differentiates from siblings like codereview or secaudit by being a systematic validation, but does not explicitly exclude them.
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 lists use cases ('multi-repository validation, security review, change impact assessment, and completeness verification') but fails to provide guidance on when not to use this tool or suggest alternative sibling tools for narrower tasks like pure security audits.
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?
Annotations already declare readOnlyHint=true, and the description aligns with that. The description adds context about 'structured analysis with expert validation', hinting at a multi-step process, but does not fully disclose the iterative step-based workflow implied by the schema (step_number, total_steps, next_step_required).
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 just three sentences, front-loaded with the core purpose. It is concise and free of redundancy, though it could be slightly more structured to outline the multi-step process.
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 (20 parameters, 6 required, multi-step protocol), the description is too brief. It does not explain the iterative step nature, how to use step_number/total_steps, or the role of expert validation, leaving significant gaps for an AI agent to infer from the schema alone.
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 all 20 parameters thoroughly. Baseline is 3, and the tool description does not add additional parameter meaning 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 states the tool's purpose: 'Analyzes code for refactoring opportunities with systematic investigation' and lists specific use cases (code smell detection, decomposition planning, modernization, maintainability improvements). It differentiates from siblings like 'codereview' and 'analyze' by emphasizing systematic investigation and expert validation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use for code smell detection, decomposition planning, modernization, and maintainability improvements', providing clear usage context. However, it lacks explicit guidance on when not to use or mention of alternatives among siblings.
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?
Annotations already declare readOnlyHint=true, indicating no state modification. The description adds that the tool consults multiple models, which implies external calls but does not detail potential costs, rate limits, or other side effects. There is no contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences front-loaded with the core action. Every sentence adds value with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the multi-step consensus process and lack of an output schema, the description gives a general idea but omits procedural details and return value format. It does not fully prepare users for the step-based workflow evident in the input schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already thoroughly explains each parameter. The tool description provides a high-level overview but does not add significant new semantic information 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: building multi-model consensus through analysis and debate. It lists specific use cases like complex decisions, architectural choices, feature proposals, and technology evaluations, effectively distinguishing it from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear guidance on when to use the tool for complex decisions and evaluations. However, it does not explicitly mention when not to use it or direct users to alternative tools, though the context is sufficient for appropriate use.
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?
Annotations already indicate readOnlyHint=true; description adds 'generates' which is consistent but does not disclose further behavioral details such as auth requirements or limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences that front-load the purpose and use cases; no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the high parameter count and no output schema, the description is sufficient for a high-level understanding but lacks details on workflow or step-by-step 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%, so description does not need to add parameter details; it provides no additional meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates code documentation with systematic analysis of functions, classes, and complexity, distinguishing it from siblings like codereview and testgen.
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 lists explicit use cases (documentation generation, code analysis, complexity assessment, API documentation) but does not provide when-not-to-use or compare directly to 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?
Annotations already declare readOnlyHint=true. Description adds value by explaining the tool's systematic approach, expert validation, and structured investigation process, which aligns with read-only analysis without contradicting 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?
Three sentences front-loaded with primary purpose and use cases. No extraneous information; each sentence adds value. Could be slightly more concise but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's 22 parameters and lack of output schema, the description covers the high-level purpose but does not explain the multi-step process or how continuation_id works, which is partially covered in schema. Adequate but not comprehensive.
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 parameter-specific details beyond what is already in the schema, which is adequate for a read-heavy tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it performs comprehensive security audit with systematic vulnerability assessment, and lists specific use cases (OWASP Top 10, compliance, threat modeling). It distinguishes well from sibling tools like analyze or codereview by focusing solely on security audit tasks.
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 states when to use: for OWASP Top 10 analysis, compliance evaluation, threat modeling, and security architecture review. Provides context for structured security investigation, but does not mention when not to use or name alternatives like analyze or codereview.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description extensively discloses behavioral traits beyond annotations: it outlines a structured investigation process, explains parameter importance (e.g., confidence levels with warnings), and notes the continuation_id for multi-turn conversations. No contradiction with readOnlyHint.
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, front-loaded with purpose and usage guidance. Every sentence adds value without redundancy, achieving high conciseness.
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 (17 parameters, no output schema), the description plus schema provide sufficient context for investigation. It lacks explicit detail on return format or post-analysis steps, but the workflow is adequately conveyed.
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 provides 100% documentation coverage for all 17 parameters with detailed descriptions. The tool's free-form description adds no extra parameter info beyond what the schema gives, so baseline 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 performs systematic debugging and root cause analysis for various issues (complex bugs, errors, performance issues, etc.). It uses a specific verb-phrase and lists example use cases, but does not explicitly differentiate from sibling tools like 'analyze' or 'codereview'.
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 guidance on when to use the tool: 'Use for complex bugs, mysterious errors, performance issues, race conditions, memory leaks, and integration problems.' This sets clear context, though it omits when not to use or alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true, confirming no side effects. The description adds behavioral context by specifying it forces critical thinking and is used for sanity-checking, which aligns with the read-only nature. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with core purpose, no redundancy. Every word is necessary and informative.
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 single parameter with thorough schema description, read-only annotation, and no output schema, the description covers the essential context: when and how to use. Lacks mention of output format, but that is not critical for this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter 'prompt', which already provides detailed instructions (stripping 'challenge' for manual invocations). The tool description does not add further 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 purpose: to prevent reflexive agreement by forcing critical thinking and reasoned analysis when a statement is challenged. It identifies the specific action (scrutinizing statements) and distinguishes its role among siblings like 'analyze' and 'thinkdeep'.
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 states when to use: automatically when a user critically questions or disagrees, and manually for sanity-checking contentious claims. Does not explicitly mention when not to use or alternative siblings, but the guidance is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the behavioral core is covered. The description adds value by specifying what information is returned (providers, names, aliases, capabilities). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that conveys all necessary information without fluff. It is front-loaded with the verb and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no parameters and no output schema, the description is sufficient. It explains what is shown, though it could optionally mention output format (e.g., list).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero parameters and 100% schema coverage (empty schema), the description need not add parameter details. The no-param baseline is 4, and the description does not need to compensate further.
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 uses a specific verb 'Shows' and clearly identifies the resource: AI model providers, names, aliases, and capabilities. It distinguishes this tool from siblings which are action-oriented (analyze, challenge, etc.).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when one needs to see available models, but does not provide explicit when-to-use or when-not-to-use guidance, nor does it reference alternatives. The context of sibling tools is broad, but no exclusion criteria are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, and the description reinforces this by describing analysis activities (investigation, reasoning, hypothesis testing) without any implication of state modification. The description adds behavioral context about multi-stage processing and expert validation that annotations alone do not provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with no fluff: first states purpose, second lists use cases, third summarizes capabilities. Every sentence adds value. The description is front-loaded with the most critical 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?
Despite having 19 parameters and no output schema, the description provides adequate high-level context about the tool's multi-stage nature and systematic approach. The schema fields (step, step_number, findings, etc.) further clarify the workflow. Missing details about output format are compensated by the schema's explicitness.
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 all parameter meanings comprehensively. The description does not add parameter-level details beyond the schema, but that is acceptable since the schema descriptions are thorough. Baseline 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's specific verb-resource combination: 'Performs multi-stage investigation and reasoning' for complex problem analysis. It lists concrete use cases (architecture decisions, complex bugs, performance challenges, security analysis), effectively distinguishing it from sibling tools like analyze, debug, or secaudit which are more focused or single-stage.
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, actionable guidance on when to use this tool by listing appropriate scenarios. However, it does not explicitly state when not to use it (e.g., simple lookups) or compare to similar siblings. Despite this, the context is sufficiently clear for an AI agent to make appropriate 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?
Annotations already mark it as readOnlyHint=true. The description adds specific behaviors: retrieving version, config, and tool list. No contradictions and adds useful context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, direct, no filler. Every word 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 zero parameters and no output schema, the description fully explains what the tool returns (version, config, tool list). No missing 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?
The input schema has zero parameters, so baseline is 4. The description does not need to explain parameters and focuses on the output, which 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 explicitly states the tool retrieves server version, configuration details, and list of available tools. It clearly distinguishes from sibling tools like 'listmodels' (which likely lists models) and other operational tools.
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 obtaining server metadata but does not provide explicit when-to-use or when-not-to-use guidance. Alternatives are not mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, and description adds context about searching authoritative sources and ensuring up-to-date accuracy. No contradictions; adequately describes behavior for a read-only search tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no fluff. Front-loaded with usage instruction. 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 simple tool with one param and no output schema, description covers enough. Could mention what happens if search fails, but otherwise complete for the tool's purpose.
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?
Only one parameter 'prompt' with schema coverage 100%. Description adds value by listing examples of what to include (API, SDK, library, etc.), beyond the schema's generic description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool provides current API/SDK documentation, version info, breaking changes, etc. Specific verb 'search' and resource types listed. Distinct from sibling tools like analyze or chat.
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 tool automatically when you need current API/SDK documentation...' providing clear context for use. Does not mention when not to use or alternatives, but usage context is well-defined.
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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