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Gitingest MCP Server

by mattdepaolis
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Python
Hybrid

Gitingest MCP server

An MCP server for gitingest that provides access to Git repository analysis through the Model Context Protocol (MCP). This server leverages the gitingest library to analyze Git repositories and make their content available in a format optimized for LLMs.

Overview

This MCP server provides a single unified tool for accessing Git repository data. It automatically handles repository ingestion as needed, so users can immediately query repository content without an explicit ingestion step.

Related MCP server: GitHub MCP Server

Tool: gitingest

The server provides a single tool called gitingest that can be used to analyze Git repositories. The tool accepts the following parameters:

  • repo_uri (required): URL or local path to the Git repository

  • resource_type: Type of data to retrieve (summary, tree, content, or all). Default is summary.

  • max_file_size: Maximum file size in bytes to include in the analysis. Default is 10MB.

  • include_patterns: Comma-separated patterns of files to include in the analysis.

  • exclude_patterns: Comma-separated patterns of files to exclude from the analysis.

  • branch: Specific branch to analyze.

  • output: File path to save the output to.

  • max_tokens: Truncates the response to a specified number of tokens.

Accessing Private Repositories

You can ingest private GitHub repositories by providing a GitHub Personal Access Token (PAT).

Recommended: Set an Environment Variable in your MCP Config

This is the best approach for persistent configuration. Add an env block to your server definition in your MCP configuration file. The gitingest library will automatically use the GITHUB_TOKEN environment variable.

"mcpServers": {
  "mattdepaolis-gitingest-mcp": {
    "command": "uvx",
    "args": [
      "mattdepaolis-gitingest-mcp"
    ],
    "env": {
      "GITHUB_TOKEN": "github_pat_..."
    }
  }
}

Resource Types and Large Repositories

For large repositories, it's recommended to first request only the summary (which is the default). After ingestion, you can access more detailed information through the resources:

  • Use the tree resource to explore the repository structure

  • Use the content resource to access the full content (if not too large)

If the repository is too large, consider using include_patterns and/or exclude_patterns to limit the scope of the ingestion.

Accessing Resources After a Tool Call

After you call the gitingest tool for a repository, the server defines resources for that repository:

  • Summary: A high-level summary of the repository

  • Tree: The file/directory structure

  • Content: The full content (subject to size limits)

These resources can be accessed individually via the resources interface in any MCP-compatible client. This is useful for browsing or fetching specific aspects of a repository after ingestion.

Debugging

The best way to debug MCP servers is with the MCP Inspector.

You can launch the Inspector with your local server using this command:

npx @modelcontextprotocol/inspector uv --directory /Users/matthias/Desktop/ai_projects/MCP-Server-Gitingest/mattdepaolis-gitingest-mcp run mattdepaolis-gitingest-mcp

or using uvx for the mcp server:

npx @modelcontextprotocol/inspector uvx https://github.com/mattdepaolis/mattdepaolis-gitingest-mcp.git

or using the PyPI package:

npx @modelcontextprotocol/inspector uvx mattdepaolis-gitingest-mcp

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

Available Tools

1 tool
gitingestB

Access Git repository data with automatic ingestion as needed

ParametersJSON Schema
NameRequiredDescriptionDefault
repo_uriYesURL or local path to the Git repository
resource_typeNoType of data to retrieve (default: summary)
max_file_sizeNoMaximum file size in bytes (default: 10MB)
include_patternsNoComma-separated fnmatch-style glob patterns (e.g., 'src/module/*.py', 'docs/file.md').
exclude_patternsNoComma-separated fnmatch-style glob patterns (e.g., 'tests/*', '*.tmp').
branchNoSpecific branch to analyze
outputNoFile path to save the output to
max_tokensNoMaximum number of tokens to return (1 token = 4 characters). If set, response will be truncated.

TDQS

B3.1/5.0
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 'automatic ingestion as needed' which hints at some background processing behavior, but doesn't explain what 'ingestion' entails (e.g., cloning, indexing, caching), performance characteristics, error handling, or what happens when repository data is already available. For a tool with 8 parameters and 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that communicates the core functionality without waste. It's appropriately sized for the tool's complexity and gets straight to the point without unnecessary elaboration.

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 8 parameters, no annotations, and no output schema, the description is minimal. While the schema covers parameters well, the description doesn't address behavioral aspects, return values, or usage context that would help an agent understand when and how to invoke this tool effectively. It's adequate but leaves clear gaps in understanding the tool's full behavior.

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 all 8 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. According to the rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

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: 'Access Git repository data with automatic ingestion as needed'. It specifies the verb ('access') and resource ('Git repository data'), and mentions the automatic ingestion feature. However, without sibling tools, it cannot demonstrate differentiation from alternatives, so it doesn't reach the highest 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 alternatives, prerequisites, or constraints. It mentions 'automatic ingestion as needed' but doesn't explain what triggers ingestion or when manual intervention might be required. There are no explicit when/when-not instructions or named alternatives.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev1.1.2
    • First observedgitingest
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TDQS

B3.2/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'gitingest' has a clearly defined purpose of accessing Git repository data with automatic ingestion, making it impossible for an agent to confuse it with other tools.

Naming Consistency5/5

Since there is only one tool, naming consistency is inherently perfect. The tool name 'gitingest' follows a single pattern with no deviations or mixing of conventions to evaluate, ensuring predictability.

Tool Count2/5

A single tool is generally too few for a server's purpose, as it limits functionality and suggests an incomplete or overly narrow scope. For a Git-related server, one tool is insufficient to cover typical operations like cloning, committing, or branching, making it feel thin and underdeveloped.

Completeness2/5

The tool surface is severely incomplete for a Git domain. While 'gitingest' handles data access and ingestion, it lacks essential operations such as repository management, version control actions, or querying specific Git objects, leading to significant gaps that will likely cause agent failures in broader workflows.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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