gitingest 와 통합되어 모든 Git 저장소를 코드베이스의 간단한 텍스트 다이제스트 형태로 변환하는 MCP(Model Context Protocol) 서버 구현입니다.
모델 컨텍스트 프로토콜을 통한 AI 어시스턴트와의 쉬운 통합
Git 저장소 분석 및 수집 기능
크기, 패턴 및 분기별 파일 필터링 지원
요약, 파일 구조 및 콘텐츠를 포함한 포괄적인 저장소 정보를 반환합니다.
Related MCP server: GitHub MCP Server
gitingest-mcp를 MCP 서버로 활성화하려면 AI 어시스턴트 설정에 다음 구성을 추가하세요.
지엑스피1
{
"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"
]
}
}
}서버는 다음과 같은 도구를 제공합니다.
ingest_gitGit 저장소를 분석하고 구조화된 형식으로 콘텐츠를 반환합니다.
source : Git 저장소의 URL 또는 로컬 디렉토리 경로
max_file_size (선택 사항): 허용되는 최대 파일 크기(바이트)(기본값: 10MB)
include_patterns (선택 사항): 포함할 파일을 지정하는 패턴 또는 패턴 세트(예: "*.md, src/")
exclude_patterns (선택 사항): 제외할 파일을 지정하는 패턴 또는 패턴 세트
branch (선택 사항): 복제하고 분석할 브랜치(기본값: "main")
다음을 포함하는 문자열:
저장소 요약
파일의 트리형 구조
저장소 파일의 내용
gitingest 웹사이트: https://gitingest.com/
gitingest 저장소: https://github.com/cyclotruc/gitingest
자세한 내용은 LICENSE 파일을 참조하세요.
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.
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