CodeCompass MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but there is some overlap between analyze_codebase and review_code, as both involve code analysis and could be confused for similar tasks. The descriptions help differentiate them, but the boundaries are not perfectly clear.
Naming Consistency4/5
Tool names mostly follow a consistent verb_noun pattern (e.g., analyze_codebase, get_file_content, search_repository), with minor deviations like health_check (noun_verb) and suggest_improvements (verb_noun_plural). The naming is readable and largely predictable.
Tool Count5/5
With 11 tools, the count is well-scoped for a code analysis and repository management server. Each tool appears to serve a specific function, and the number is reasonable for covering the domain without being overwhelming.
Completeness4/5
The tool set covers core aspects of code analysis, repository exploration, and AI-powered insights, but there are minor gaps such as no explicit update or delete operations for repository data, which might limit full lifecycle management. However, agents can likely work around this with the provided tools.
Average 3.2/5 across 11 of 11 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

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

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

How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but lacks critical behavioral details. It doesn't disclose whether this is a read-only operation, potential performance/rate limits, authentication needs for GitHub URLs, or what the output format looks like. The description mentions analysis types but not how results are returned or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that efficiently convey the tool's scope and capabilities. It's front-loaded with the core purpose and avoids unnecessary elaboration, though it could be slightly more structured by explicitly separating scope from output characteristics.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex analysis tool with 4 parameters, nested objects, no annotations, and no output schema, the description is insufficient. It doesn't explain what the unified view output contains, how analysis results are structured, performance considerations, or error conditions. The gap between tool complexity and description detail is significant.
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 75%, providing good parameter documentation. The description adds minimal value beyond the schema by mentioning analysis types that correspond to the 'analysis_types' parameter enum, but doesn't explain parameter interactions or provide additional context about how parameters affect the analysis.
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 'comprehensive codebase analysis' with specific analysis types (structure, architecture, metrics, patterns, complexity) and quality indicators. It distinguishes itself from siblings like 'analyze_dependencies' or 'review_code' by offering a unified multi-faceted analysis, though it doesn't explicitly contrast with each sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'analyze_dependencies' for dependency analysis, 'review_code' for code review, or 'explain_code' for explanations. It mentions analysis types but doesn't specify use cases or exclusions relative to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it lists analysis components (security, versions), it doesn't describe what the tool actually returns (e.g., report format, error handling), whether it makes network calls, has rate limits, or requires authentication. For a tool with no annotation coverage, this leaves significant behavioral gaps.
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, efficient sentence that front-loads the core purpose ('comprehensive dependency analysis') followed by scope details. There's no wasted text, though it could be slightly more structured (e.g., separating scope items with commas instead of 'and').
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 (dependency analysis with security and version checks), no annotations, no output schema, and incomplete parameter documentation (50% schema coverage), the description is inadequate. It doesn't explain what the analysis returns, how results are structured, or address the undocumented 'options' parameter, leaving the agent with insufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (only the 'url' parameter has a description in the schema). The description doesn't mention any parameters or their semantics, failing to compensate for the coverage gap. With 2 parameters (one required, one nested object with 4 sub-parameters), the description adds no value beyond what the schema provides, resulting in a baseline score.
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 'comprehensive dependency analysis' and specifies the scope: 'external packages, internal dependencies, security vulnerabilities, and version conflicts.' It uses a specific verb ('analyze') with a resource ('dependencies'), but doesn't explicitly differentiate from sibling tools like 'analyze_codebase' or 'review_code' that might also examine dependencies.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'analyze_codebase', 'review_code', and 'get_repository_info' that might overlap, there's no indication of when this specific dependency analysis is preferred or what prerequisites exist (e.g., requires a GitHub repository URL).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'real-time insights' and 'comprehensive monitoring dashboard' but doesn't specify whether this is a read-only operation, what permissions might be required, whether it affects system performance, or what the response format looks like. For a monitoring tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
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 appropriately concise with two sentences that efficiently convey the core purpose. The emoji adds visual distinction but doesn't waste space. Every sentence contributes value, though the second sentence could be more specific about what 'dashboard' means in this context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a monitoring tool with 2 parameters, nested objects, no annotations, and no output schema, the description is insufficient. It doesn't explain what kind of data is returned, how to interpret results, whether this is a safe operation, or what the monitoring scope includes. The description should provide more context about the tool's behavior and output given the lack of structured metadata.
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 50% (only the 'checks' parameter has a description in schema). The description adds no specific parameter information beyond what's implied by 'comprehensive monitoring dashboard.' It doesn't explain what the 'checks' array represents or what the 'options' object controls. The description doesn't compensate for the schema coverage gap, but the baseline is 3 since the schema provides some parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Monitor server health, performance, and operational metrics' with 'Provides comprehensive monitoring dashboard with real-time insights.' It specifies the verb (monitor) and resource (server health/performance/metrics). However, it doesn't explicitly distinguish this health monitoring tool from its siblings which are all code analysis tools, though the domain difference is obvious.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, timing considerations, or compare it to any other monitoring or diagnostic tools. The context signals show sibling tools are all code-focused, but the description doesn't help an agent understand when health monitoring is appropriate versus code analysis.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions the tool provides 'analysis, security scanning, and actionable recommendations,' it doesn't describe what happens during execution (e.g., whether it modifies code, requires authentication, has rate limits, or returns structured vs. free-text results). For a complex tool with 5 parameters and no output schema, this leaves significant behavioral gaps.
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, efficient sentence that packs substantial information about the tool's capabilities. It uses emoji and clear language without unnecessary elaboration. However, it could be more front-loaded with the core purpose rather than starting with an emoji, and it might benefit from slightly more structure given the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 5 parameters, nested objects, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, how results are structured, error conditions, or performance characteristics. The description focuses on what the tool does at a high level but lacks the detail needed for an agent to understand the full context of using this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 80%, providing good documentation for most parameters. The description adds minimal value beyond the schema, mentioning 'AI insights' and 'rule-based validation' which loosely relate to the 'review_mode' parameter but doesn't elaborate on parameter interactions or usage patterns. With high schema coverage, the baseline of 3 is appropriate as the description doesn't significantly enhance parameter understanding.
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 'comprehensive code review combining AI insights with rule-based validation' and lists specific outputs like 'intelligent analysis, security scanning, and actionable recommendations.' This provides a specific verb (review) and resource (code) with scope details. However, it doesn't explicitly differentiate from sibling tools like 'analyze_codebase' or 'suggest_improvements,' which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'analyze_codebase' or 'suggest_improvements.' It mentions what the tool does but doesn't specify scenarios where it's preferred over other code analysis tools in the server. There's no mention of prerequisites, limitations, or typical use cases that would help an agent choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'advanced filtering options' but doesn't specify what happens during execution - whether it's read-only, if it requires authentication, rate limits, pagination behavior, or what the output format looks like. For a search tool with complex parameters, this leaves significant behavioral gaps.
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, efficient sentence that clearly states the core purpose. It's appropriately sized and front-loaded with the main function. There's no wasted verbiage, though it could potentially benefit from slightly more detail given the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 4 parameters (including a complex nested object), no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, how results are structured, or provide behavioral context about execution. The single sentence description leaves too many questions unanswered for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions 'advanced filtering options' which aligns with the 'options' parameter in the schema, but doesn't add meaningful semantics beyond what the 75% schema coverage already provides. The schema descriptions are quite detailed for most parameters, so the description adds minimal value. With good schema coverage, 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 searches across an entire repository for patterns, text, functions, or classes with advanced filtering. It specifies the verb 'search' and resource 'repository' but doesn't explicitly differentiate from sibling tools like 'analyze_codebase' or 'explain_code' which might have overlapping search capabilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'analyze_codebase', 'explain_code', and 'get_file_content' that might involve searching, there's no indication of when this specific search tool is preferred or what distinguishes it from other tools that could retrieve repository information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'AI-powered improvement suggestions' but lacks details on permissions needed, rate limits, whether it modifies code (likely read-only analysis), output format, or error handling. This is inadequate for a tool with 5 parameters and no output schema.
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, efficient sentence with an emoji for visual emphasis. It's front-loaded with the core purpose and avoids redundancy. Every word contributes to conveying the tool's function without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, nested objects, no output schema, and no annotations), the description is insufficient. It doesn't address behavioral aspects like whether it's read-only or mutative, what the output looks like, or how it differs from siblings. For an AI-powered analysis tool with multiple inputs, more context is needed.
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 80%, so the baseline is 3. The description adds no specific parameter semantics beyond what's in the schema—it doesn't explain relationships between parameters like how 'improvement_goals' interacts with 'target_framework' or 'options'. The schema already documents parameters well, but the description doesn't enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'AI-powered improvement suggestions providing strategic refactoring recommendations, modernization plans, and architectural enhancements.' It specifies the verb ('suggest improvements') and resource type (codebase improvements), though it doesn't explicitly differentiate from siblings like 'review_code' or 'transform_code' beyond the AI-powered aspect.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like 'review_code' or 'transform_code' is provided. The description implies usage for AI-driven improvement analysis but doesn't specify prerequisites, exclusions, or comparative contexts with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses several behavioral traits: smart truncation, batch processing, automatic validation/security checks, metadata extraction, configurable limits, error handling, and format support. However, it lacks details on authentication requirements, rate limits, error types, or what 'smart truncation' specifically entails.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose but uses a feature-list format that's somewhat redundant. Sentences like 'Automatic file validation and security checks' and 'Configurable processing limits and error handling' could be more integrated. The emoji and formatting add visual noise without substantive value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters (including a complex nested object), no annotations, and no output schema, the description provides moderate context. It covers key capabilities but lacks details on return format, error responses, authentication, and specific limitations. Given the complexity, it should do more to guide 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 description coverage is 67%, and the description adds little parameter-specific information beyond what's in the schema. It mentions 'batch processing' (implied by file_paths array) and 'configurable processing limits' (implied by options.max_concurrent), but doesn't explain parameter interactions or provide examples. The description doesn't compensate for the 33% coverage gap.
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 retrieves file content with specific capabilities (smart truncation, batch processing). It distinguishes from siblings like get_file_tree (structure) or analyze_codebase (analysis) by focusing on content retrieval. However, it doesn't explicitly contrast with all siblings like search_repository.
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 lists features but provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose get_file_content over get_file_tree for file exploration or search_repository for finding files. No explicit when/when-not instructions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions transformation types but lacks critical behavioral details: whether transformations are destructive (could break code), what permissions or authentication might be needed, rate limits, error handling, or what the output looks like (since no output schema exists). The description is insufficient for a mutation tool with complex parameters.
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 appropriately concise with two sentences that efficiently convey the tool's scope. The first sentence lists transformation types clearly, and the second adds useful context about combining syntax and structural changes. No wasted words, though it could be slightly more front-loaded with core purpose.
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 (6 parameters with nested objects, mutation functionality), absence of annotations, and no output schema, the description is incomplete. It doesn't address critical context like what the transformation output contains, error conditions, safety considerations, or how to interpret results. For a code transformation tool with significant behavioral implications, this leaves too many gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 83% (high), so the baseline is 3 even without parameter details in the description. The description adds minimal value beyond the schema by listing transformation categories (syntax, structural, framework, modernization) that align with the 'transformations.type' enum, but doesn't explain parameter interactions or provide examples. It doesn't compensate for the 17% coverage gap.
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 with specific verbs ('apply code transformations') and resources ('code'), listing concrete transformation types (syntax changes, structural reorganization, framework migration, modernization). It distinguishes from siblings like analyze_codebase, explain_code, or suggest_improvements by emphasizing active transformation rather than analysis or suggestion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. While it mentions 'combines syntax-level and structural changes,' it doesn't specify when to choose transform_code over suggest_improvements (which might recommend changes) or review_code (which might assess code quality). No prerequisites, exclusions, or comparison to sibling tools are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'AI-powered' and 'transforms technical analysis,' hinting at generative AI behavior, but lacks critical details: whether it makes network calls to external services, potential rate limits, authentication needs, output format (text/markdown?), or error handling. For a tool with AI integration and no annotations, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized (two sentences) and front-loaded with the core purpose ('AI-powered code explanation...'). Every sentence adds value: the first defines the tool's function, and the second emphasizes transformation into accessible content. There's zero waste or redundancy, making it highly efficient for quick comprehension.
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 (AI integration, 4 parameters with nested objects) and lack of both annotations and output schema, the description is incomplete. It doesn't address behavioral aspects like AI model usage, output format, or error cases, leaving gaps for the agent. For a tool with these characteristics, the description should provide more context to compensate for missing structured data.
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 75% (good), so the baseline is 3 even without parameter details in the description. The description adds no specific parameter semantics beyond what's in the schema—it doesn't clarify URL formats, file_paths selection logic, or explanation_type nuances. However, with high schema coverage, the description isn't expected to compensate heavily, maintaining an adequate baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'AI-powered code explanation generating human-readable documentation, tutorials, and architectural insights' with the verb 'explains' implied. It specifies the resource as 'code' and distinguishes from siblings like analyze_codebase or review_code by focusing on explanation rather than analysis or review. However, it doesn't explicitly differentiate from suggest_improvements which might also involve explanation.
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 context through terms like 'human-readable documentation' and 'accessible explanations,' suggesting it's for making code understandable. However, it provides no explicit guidance on when to use this tool versus alternatives like analyze_codebase (for technical analysis) or review_code (for code quality assessment). The agent must infer appropriate usage from the description's focus on explanation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions the tool is 'focused on file system structure without content analysis,' it doesn't describe what the output looks like (tree format, JSON structure), whether it's paginated, rate limits, authentication requirements, or error conditions. For a tool with 2 parameters (one being a complex nested object), this is insufficient 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with two sentences that each earn their place. The first sentence states the core purpose with filtering context, and the second sentence provides important scope limitation. No wasted words, and the information is front-loaded appropriately.
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 has 2 parameters (one complex nested object with 5 sub-properties), no annotations, and no output schema, the description is inadequate. It doesn't explain the output format, doesn't clarify the relationship between parameters, and leaves most behavioral aspects unspecified. For a tool that presumably returns structured directory data, this creates significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (only the 'url' parameter has a description in the schema). The description mentions 'filtering options' which aligns with the 'options' parameter, but doesn't explain what specific filters are available or their semantics. It adds minimal value beyond what's implied by the parameter names in the schema, resulting in a baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('complete directory structure and file listing') with specific scope ('without content analysis'). It distinguishes from siblings like get_file_content (which retrieves file contents) and analyze_codebase (which performs content analysis). The emoji reinforces the tree structure concept.
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 about when to use this tool ('Focused on file system structure without content analysis'), which implicitly distinguishes it from content-focused siblings like analyze_codebase and explain_code. However, it doesn't explicitly state when NOT to use it or name specific alternative tools for different scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It describes the tool's scope ('repository-level data without file content analysis') but doesn't disclose behavioral traits like rate limits, authentication requirements, error conditions, or what happens with invalid URLs. It provides some context but leaves significant behavioral aspects unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just two sentences that are front-loaded with the core purpose. Every word earns its place - the first sentence states what it does, the second provides crucial differentiation from sibling tools. No wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 2 parameters (one required), no annotations, no output schema, and 50% schema coverage, the description provides adequate purpose and usage guidance but lacks details about return values, error handling, authentication, or rate limits. It's minimally complete for basic understanding but leaves important contextual 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?
With 50% schema description coverage (only the 'url' parameter has a description in schema), the description doesn't mention any parameters directly. However, it implies the tool returns 'metadata, statistics, and key information' which aligns with the options parameters. The description adds some semantic context about what information can be retrieved, partially compensating for the schema coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'repository metadata, statistics, and key information', distinguishing it from siblings by specifying it's 'purely on repository-level data without file content analysis'. This explicitly differentiates it from tools like get_file_content or analyze_codebase.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance by stating this is for 'basic repository metadata' and is 'focused purely on repository-level data without file content analysis', clearly indicating when to use this tool versus alternatives like analyze_codebase or get_file_content that handle file-level analysis.
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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