模型上下文协议 (MCP) 服务器实现与gitingest集成，将任何 Git 存储库转换为其代码库的简单文本摘要。
通过模型上下文协议轻松与人工智能助手集成
Git 存储库分析和提取功能
支持按大小、模式和分支过滤文件
返回全面的存储库信息，包括摘要、文件结构和内容
Related MCP server: GitHub MCP Server
将以下配置添加到您的 AI 助手的设置中，以启用 gitingest-mcp 作为 MCP 服务器：
{
"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"
]
}
}
}服务器提供以下工具：
ingest_git分析 Git 存储库并以结构化格式返回其内容。
source ：Git 存储库的 URL 或本地目录路径
max_file_size （可选）：允许的最大文件大小（以字节为单位）（默认值：10MB）
include_patterns （可选）：指定要包含的文件的模式或模式集（例如“*.md，src/”）
exclude_patterns （可选）：指定要排除的文件的模式或模式集
branch （可选）：要克隆和分析的分支（默认值：“main”）
包含以下内容的字符串：
存储库摘要
文件的树状结构
存储库文件的内容
gitingest网站： https://gitingest.com/
gitingest 存储库： https://github.com/cyclotruc/gitingest
有关详细信息，请参阅LICENSE文件。
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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