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singhashish4000

Git Analytics MCP Server

by singhashish4000
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation2/5

    The tools have significant overlap in purpose, making them hard to distinguish. Both get_repository_overview and get_repository_stats appear to provide repository-level statistics, with get_repository_stats explicitly mentioning 'commits, authors, branches, and code changes' while get_repository_overview mentions 'stats, top contributors, and recent activity'—these descriptions suggest substantial redundancy. The only clearly distinct tool is get_author_stats, which focuses on contributor-level data.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case (get_author_stats, get_repository_overview, get_repository_stats). The naming is predictable and readable, with 'get' as the verb and descriptive nouns, showing no deviations in style or convention.

    Tool Count3/5

    With only 3 tools, the server feels thin for a 'Git Analytics' domain, which typically involves more granular operations like trend analysis, commit filtering, or branch comparisons. While 3 tools can be appropriate for a minimal scope, the overlapping descriptions suggest this might be under-scoped, lacking depth in analytical capabilities.

    Completeness2/5

    The tool set is severely incomplete for a Git analytics server. It only provides 'get' operations for statistics, with no tools for filtering, comparing, trending, or analyzing specific aspects like code churn, hot spots, or temporal patterns. This limits agents to basic retrieval without supporting deeper analytical workflows, creating significant gaps in functionality.

  • Average 3/5 across 3 of 3 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

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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

  • 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 describes a read operation ('get') but doesn't mention performance characteristics, rate limits, authentication needs, or what 'detailed statistics' entails in terms of output format or data scope. This leaves significant gaps for an agent to understand how the tool behaves beyond its basic function.

    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. It avoids redundancy and wastes no words, though it could be slightly more structured by separating purpose from scope details.

    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 (statistical analysis with no output schema) and lack of annotations, the description is incomplete. It doesn't explain what 'detailed statistics' includes, how data is aggregated, or the return format. For a tool with behavioral unknowns and no structured output documentation, this leaves the agent under-informed.

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

    Parameters3/5

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

    The input schema has 100% description coverage, with the single parameter 'path' well-documented in the schema. The description adds no parameter-specific information beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without compensating value.

    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 with specific verbs ('get detailed statistics') and resources ('contributors'), including what statistics are retrieved ('commits, code changes, and activity periods'). It distinguishes itself from siblings by focusing on author-level data rather than repository-level overviews or general statistics, though it doesn't explicitly name the 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 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 the sibling tools (get_repository_overview, get_repository_stats). It mentions 'contributors' which implies author-focused analysis, but doesn't specify use cases, prerequisites, or exclusions. The agent must infer usage from the purpose alone.

    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. It mentions the tool 'Get[s] a comprehensive overview' but does not specify if this is a read-only operation, potential side effects, authentication needs, rate limits, or output format. For a tool with no annotations, this leaves significant behavioral gaps, though it implies a safe read operation without being explicit.

    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 that front-loads the key action and details without any wasted words. It is appropriately sized for the tool's complexity, making it easy for an agent to parse and understand quickly.

    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 low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It states the purpose but lacks details on behavioral traits, usage context, and output, which are needed for full completeness. However, it covers the basic intent, making it functional but with clear gaps in guidance and transparency.

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

    Parameters3/5

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

    The input schema has 100% description coverage, with the 'path' parameter well-documented in the schema itself. The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.

    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 with specific verbs ('Get') and resources ('Git repository'), and it details what information is included ('stats, top contributors, and recent activity'). However, it does not explicitly differentiate from sibling tools like 'get_author_stats' or 'get_repository_stats', which might offer overlapping or complementary functionality, preventing a perfect score.

    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 its siblings ('get_author_stats' and 'get_repository_stats'). It lacks explicit instructions on alternatives, prerequisites, or exclusions, leaving the agent to infer usage based on tool names alone, which is insufficient for clear decision-making.

    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. It states the tool retrieves statistics but doesn't describe behavioral traits such as whether it's read-only (implied by 'Get'), performance characteristics, error handling, or output format. For a tool with zero annotation coverage, 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 a single, efficient sentence that front-loads the core purpose and lists key statistics without unnecessary words. Every part of the sentence adds value, making it appropriately sized and well-structured.

    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 moderate complexity (retrieving multiple statistics), no annotations, and no output schema, the description is minimally adequate. It specifies what statistics are included but lacks details on behavioral traits, usage context, or output format. This leaves gaps that could hinder an agent's ability to use the 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 input schema has 100% description coverage, with the single parameter 'path' well-documented in the schema itself. The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints. With high schema coverage, the baseline score of 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 the tool's purpose with a specific verb ('Get') and resource ('detailed repository statistics'), and lists the types of statistics included (commits, authors, branches, code changes). However, it doesn't explicitly differentiate from sibling tools like 'get_author_stats' or 'get_repository_overview', which might offer overlapping or related 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 its siblings ('get_author_stats' and 'get_repository_overview'). It doesn't mention any prerequisites, exclusions, or alternative scenarios, leaving the agent to infer usage based on tool names alone.

    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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