Eine Model Context Protocol (MCP)-Serverimplementierung, die sich in gitingest integrieren lässt, um jedes Git-Repository in eine einfache Textzusammenfassung seiner Codebasis umzuwandeln.
Einfache Integration mit KI-Assistenten durch das Model Context Protocol
Analyse- und Aufnahmefunktionen für Git-Repositorys
Unterstützung für das Filtern von Dateien nach Größe, Mustern und Zweigen
Gibt umfassende Repository-Informationen zurück, einschließlich Zusammenfassungen, Dateistruktur und Inhalt
Related MCP server: GitHub MCP Server
Fügen Sie den Einstellungen Ihres KI-Assistenten die folgende Konfiguration hinzu, um gitingest-mcp als MCP-Server zu aktivieren:
{
"mcpServers": {
"gitingestmcp": {
"command": "uvx",
"args": ["-U", "gitingestmcp"]
}
}
}{
"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"
]
}
}
}Der Server stellt das folgende Tool bereit:
ingest_gitAnalysiert ein Git-Repository und gibt seinen Inhalt in einem strukturierten Format zurück.
source : Die URL eines Git-Repositorys oder ein lokaler Verzeichnispfad
max_file_size (optional): Maximal zulässige Dateigröße in Bytes (Standard: 10 MB)
include_patterns (optional): Muster oder Mustersatz, der die einzuschließenden Dateien angibt (z. B. „*.md, src/“)
exclude_patterns (optional): Muster oder Mustersatz, der die auszuschließenden Dateien angibt
branch (optional): Der zu klonende und zu analysierende Zweig (Standard: „main“)
Eine Zeichenfolge mit:
Repository-Zusammenfassung
Baumartige Struktur der Dateien
Inhalt der Repository-Dateien
gitingest-Website: https://gitingest.com/
Gitingest-Repository: https://github.com/cyclotruc/gitingest
Weitere Einzelheiten finden Sie in der Datei LICENSE .
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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