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klimadev

MCP Codebase Symbols Server

by klimadev
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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation4/5

    The two tools have distinct primary purposes: analyze_codebase focuses on extracting symbols and structure for LLM understanding, while read_codebase returns full file content in a structured format. However, both involve reading codebases with .gitignore respect, which could cause some confusion about when to use each, though their descriptions clearly differentiate between symbol analysis vs. full content retrieval.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with snake_case naming (analyze_codebase and read_codebase). The verbs 'analyze' and 'read' are distinct yet related, maintaining a predictable and readable convention throughout the tool set.

    Tool Count3/5

    With only 2 tools, the server feels thin for a codebase symbols domain, as it might lack operations like filtering symbols, updating analyses, or handling specific file types. While the tools cover core reading and analysis, the count is borderline minimal for typical MCP server scopes, which often include 3-15 tools for comprehensive functionality.

    Completeness3/5

    The server provides basic read and analyze operations, but there are notable gaps for a symbols-focused domain. Missing tools might include querying specific symbols, updating symbol databases, or handling incremental changes, which could limit agent workflows. However, the existing tools cover fundamental needs for codebase understanding, allowing agents to work around some limitations.

  • Average 4.1/5 across 2 of 2 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.

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It describes key behaviors: returns comprehensive markdown, respects .gitignore rules, and is optimized for LLMs. However, it doesn't address potential limitations like performance characteristics, error conditions, or what happens with invalid paths. It provides useful context but lacks complete behavioral transparency.

    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 sized with two sentences that each serve distinct purposes: the first describes the core functionality, the second provides usage context. It's front-loaded with the main purpose. While efficient, it could be slightly more structured by explicitly separating functional description from usage guidance.

    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 moderate complexity (codebase analysis), no annotations, and no output schema, the description does a good job covering the essential aspects: what it does, what it returns, and key behavioral constraints (.gitignore). However, it doesn't describe the output format in detail (beyond 'LLM-optimized markdown') or potential limitations, leaving some gaps in completeness.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100% with the single 'path' parameter well-documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides (it doesn't mention the path parameter at all). Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose with specific verbs ('analyzes', 'returns') and resources ('codebase', 'LLM-optimized markdown with all symbols'). It explicitly distinguishes from the sibling tool 'read_codebase' by emphasizing comprehensive analysis versus simple reading, mentioning specific symbol types and .gitignore handling.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides clear context for when to use this tool ('Perfect for giving LLMs complete understanding of code structure in a single request'), but doesn't explicitly state when NOT to use it or mention the sibling 'read_codebase' as an alternative. It implies usage for comprehensive analysis rather than basic reading, but lacks explicit exclusions.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries full burden and adds valuable behavioral context: it discloses that the tool respects .gitignore rules and warns about potentially very large responses. It doesn't cover other aspects like error handling or performance characteristics, but provides key operational guidance.

    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 efficiently structured in two sentences: the first states the core functionality, the second provides critical usage warning. Every sentence earns its place with no wasted words.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (reading entire codebases) and lack of annotations/output schema, the description provides good coverage of key behavioral aspects but doesn't explain the 'structured format' of returns or error conditions. It's mostly complete but has some gaps.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100%, so the schema already documents the 'path' parameter fully. The description adds no additional parameter information beyond what the schema provides, maintaining the baseline score of 3.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the specific action ('Reads the full content of all code files'), the resource ('code files in a directory'), and scope ('respecting .gitignore rules'). It distinguishes from the sibling 'analyze_codebase' by focusing on reading content rather than analysis.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides clear context for when to use ('use on smaller directories or specific subdirectories') due to the warning about large responses. However, it does not explicitly mention when NOT to use it or name alternatives like the sibling tool.

    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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