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Git Analytics MCP Server

Git Analytics MCP Server

by singhashish4000
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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get_repository_stats

Analyze Git repository statistics to track commits, authors, branches, and code changes for data-driven development insights.

Instructions

Get detailed repository statistics including commits, authors, branches, and code changes

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_repository_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathNoPath to the Git repository (defaults to current directory).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
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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