The Gitingest MCP Server integrates with AI assistants via Model Context Protocol (MCP) to analyze and ingest Git repositories into a structured text format.
Repository Analysis: Analyzes repositories from URLs or local paths, providing codebase insights
Flexible Filtering: Supports filtering by file size (max_file_size), patterns (include_patterns, exclude_patterns), and specific branches
Structured Output: Returns repository summaries, file tree structures, and content in an AI-friendly format
Multiple Installation Options: Available via PyPI, GitHub, or local installation with configurable settings
Analyzes Git repositories and transforms them into structured text digests, supporting filtering by file size, patterns, and branches to help AI assistants understand codebases.
Enables ingestion of GitHub repositories, extracting repository summaries, file structures, and content to provide comprehensive codebase insights.
Supports installation through PyPI, allowing the server to be easily integrated with AI assistants via package management.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Gitingest MCP Server analyze the repository at https://github.com/example/project and give me a summary of its structure"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server implementation that integrates with gitingest for turning any Git repository into a simple text digest of its codebase.
Easy integration with AI assistants through the Model Context Protocol
Git repository analysis and ingestion capabilities
Support for filtering files by size, patterns, and branches
Returns comprehensive repository information including summaries, file structure, and content
Related MCP server: GitHub MCP Server
Add the following configuration to your AI assistant's settings to enable gitingest-mcp as an MCP server:
{
"mcpServers": {
"gitingestmcp": {
"command": "uvx",
"args": ["gitingestmcp@latest"]
}
}
}{
"mcpServers": {
"gitingestmcp": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/narumiruna/gitingest-mcp",
"gitingestmcp"
]
}
}
}{
"mcpServers": {
"gitingestmcp": {
"command": "uv",
"args": [
"run",
"--directory",
"/home/<user>/workspace/gitingest-mcp",
"gitingestmcp"
]
}
}
}The server provides the following tool:
ingest_gitAnalyzes a Git repository and returns its content in a structured format.
source: The URL of a Git repository or a local directory path
max_file_size (optional): Maximum allowed file size in bytes (default: 10MB)
include_patterns (optional): Pattern or set of patterns specifying files to include (e.g., "*.md, src/")
exclude_patterns (optional): Pattern or set of patterns specifying files to exclude
branch (optional): The branch to clone and analyze (default: "main")
A string containing:
Repository summary
Tree-like structure of the files
Content of the repository files
gitingest website: https://gitingest.com/
gitingest repository: https://github.com/cyclotruc/gitingest
See the LICENSE file for details.
This function analyzes a source (URL or local path), clones the corresponding repository (if applicable), and processes its files according to the specified query parameters. It can return a summary, a tree-like structure of the files, or the content of the files.
| Name | Required | Description | Default |
|---|---|---|---|
| branch | No | The branch to clone and ingest. | main |
| exclude_patterns | No | Pattern or set of patterns specifying which files to exclude, e.q. '*.md, src/' | |
| include_patterns | No | Pattern or set of patterns specifying which files to include, e.q. '*.md, src/' | |
| max_file_size | No | Maximum allowed file size for file ingestion.Files larger than this size are ignored, by default 10*1024*1024 (10 MB). | |
| source | Yes | The source to analyze, which can be a URL (for a Git repository) or a local directory path. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions cloning and processing behaviors but omits critical details: whether it requires authentication, rate limits, side effects (e.g., local storage), error handling, or output format specifics. For a tool with potential external operations, this is insufficient disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with three sentences that efficiently outline the tool's flow: analyze source, clone if needed, process with parameters. It's front-loaded with core functionality, though slightly vague in the last sentence about return types. No wasted words, but could be tighter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a tool that performs complex operations (cloning, processing), the description is incomplete. It lacks details on authentication, rate limits, output formats, error cases, and how return types (summary, tree, content) are selected. For a 5-parameter tool with external dependencies, this leaves significant gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing detailed parameter documentation. The description adds minimal value beyond the schema, only implying that parameters control 'query parameters' for processing. It doesn't explain interactions between parameters (e.g., patterns vs. size limits) or usage nuances, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: analyzing a source, cloning repositories, and processing files with specific query parameters. It specifies the verb ('analyzes', 'clones', 'processes') and resource ('source', 'repository', 'files'), but lacks differentiation from siblings since none exist. It's not tautological but could be more specific about the 'analysis' aspect.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 exclusions. It mentions query parameters but doesn't explain scenarios for choosing summary, tree structure, or file content outputs. With no sibling tools, this is less critical, but overall usage context is minimal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'ingest_git' has a clearly defined purpose that is distinct by default.
A single tool inherently has perfect naming consistency. The tool name 'ingest_git' follows a clear verb_noun pattern, and there are no other tools to create inconsistency.
One tool is too few for the apparent scope of a Git ingestion server. The tool description suggests capabilities like cloning, processing files, and returning summaries, structures, or content, which could reasonably be split into multiple specialized tools (e.g., clone_repo, list_files, get_file_content). A single tool feels thin and may force agents to handle complex parameter parsing.
The tool covers basic ingestion and file access, but there are notable gaps for a Git domain. Missing operations include version control actions (e.g., commit, branch, diff), repository management (e.g., create, delete), and more granular file operations. Agents can work around this by using the single tool for all tasks, but it lacks lifecycle coverage.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
A MCP server built for developers enabling Git based project management with project and personal…
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