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DarkEden-coding

TreeSitter Code Structure MCP Server

by DarkEden-coding
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a clear, singular purpose focused on analyzing code structure across multiple programming languages.

    Naming Consistency5/5

    The single tool name 'analyze_code_structure' follows a clear verb_noun pattern and is descriptive. Since there is only one tool, consistency is inherently perfect with no deviations to assess.

    Tool Count2/5

    A single tool is too few for a server focused on code structure analysis, as it suggests an incomplete or overly simplistic surface. For a domain like code analysis, typical servers would include multiple tools for different aspects (e.g., parsing, querying, modifying).

    Completeness2/5

    The tool set is severely incomplete for the implied domain of code structure analysis. While the single tool provides analysis, there are obvious gaps such as tools for querying specific elements, modifying code, or handling other structural operations, which limits agent workflows.

  • Average 3.7/5 across 1 of 1 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 is passing

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  • 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?

    No annotations are provided, so the description carries the full burden. It discloses behavioral traits such as the types of analysis performed and supported languages, but it does not cover aspects like error handling, performance characteristics, or authentication needs. The description adds useful context but leaves gaps in behavioral disclosure.

    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 front-loaded with the core purpose, followed by supporting details in two efficient sentences. Every sentence adds value: the first defines the analysis scope and output, the second specifies languages and input formats. There is no wasted text.

    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 complexity of code analysis, no annotations, and no output schema, the description is moderately complete. It covers the purpose, input formats, and languages, but lacks details on output structure, error cases, or limitations. This is adequate for basic understanding but could be more comprehensive for a tool of this nature.

    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 fully documents the parameters. The description adds marginal value by mentioning the input format options (single file path or array) and supported languages, but it does not provide additional semantic details beyond what the schema already specifies. Baseline 3 is appropriate here.

    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 ('analyze the structure'), the target resource ('source code files'), and the detailed output ('classes, functions, their line numbers, nesting levels, parameters, and return types'). It also specifies supported programming languages, making the purpose highly specific and comprehensive.

    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 by specifying supported languages and input formats (single file path or array), but it does not provide explicit guidance on when to use this tool versus alternatives, prerequisites, or exclusions. With no sibling tools, this is adequate but lacks explicit contextual framing.

    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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