Codebase MCP
代码库 MCP
模型上下文协议 (MCP)服务器实现,提供使用RepoMix检索和分析整个代码库的工具。
该 MCP 允许 AI 代理(如 Cursor 的 Composer Agent)一次性自动读取和理解整个代码库,使开发人员更容易处理大型代码库,并使 AI 助手更轻松地了解项目的全面背景。
特征
📚代码库检索:以不同格式(XML、Markdown、Plain)检索整个代码库作为单个文本输出
🌐远程存储库支持:直接处理远程 GitHub 存储库
💾文件保存:将处理后的代码库保存到文件中
🔧可定制选项:使用各种选项(注释、行号、文件摘要等)控制代码库的处理方式。
Related MCP server: CodeAlive MCP
安装
来自 NPM(推荐)
# Install the package globally
npm install -g codebase-mcp
# Install RepoMix (required dependency)
codebase-mcp install来自 GitHub
# Clone the repository
git clone https://github.com/DeDeveloper23/codebase-mcp.git
# Navigate to the project directory
cd codebase-mcp
# Install dependencies
npm install
# Build the project
npm run build
# Install globally
npm install -g .
# Install RepoMix (required dependency)
codebase-mcp install与 Cursor 集成
要将此 MCP 与 Cursor 的 Composer Agent 一起使用:
打开游标IDE
点击侧边栏中的 Composer 图标
点击顶部的“MCP 服务器”按钮
点击“添加新的 MCP 服务器”
填写详细信息:
名称:
Codebase MCP(或您喜欢的任何名称)类型:
command命令:
codebase-mcp start
点击“添加”保存
添加后,MCP 将为 Composer Agent 提供三个强大的工具:
可用工具
获取代码库
目的:分析您当前的工作空间/项目
使用情况:你希望人工智能理解你的整个代码库
提示示例:“请分析我的代码库以了解其结构”
获取远程代码库
目的:获取并分析任何公共 GitHub 存储库
使用情况:您想要探索或了解其他项目
示例提示:“您能分析 github.com/username/repo 上的存储库吗?”
保存代码库
目的:将代码库分析保存到文件中以供日后使用
使用情况:您想要保留代码库快照或共享它
提示示例:“保存此代码库的分析以供稍后查看”
游标中的使用示例
以下是一些可以与 Composer Agent 一起使用的示例提示:
"Analyze my current project and explain its main components."
"Can you look at the tensorflow/tensorflow repository and explain how their testing framework works?"
"Save an analysis of my project to 'codebase-analysis.md' in markdown format."Composer Agent 将根据您的请求自动使用适当的工具。
游标外使用
启动 MCP 服务器
codebase-mcp start这将以 stdio 模式启动 MCP 服务器,任何与 MCP 兼容的客户端都可以使用它。
执照
麻省理工学院
Available Tools
3 toolsgetCodebaseC
Retrieve the entire codebase as a single text output using RepoMix
| Name | Required | Description | Default |
|---|---|---|---|
| cwd | No | Current working directory of the codebase (defaults to current dir) | |
| format | No | Output format (xml, markdown, or plain) | xml |
| ignorePatterns | No | Ignore patterns (using glob patterns, comma-separated) | |
| includeDirectoryStructure | No | Include directory structure | |
| includeFileSummary | No | Include summary of each file | |
| includePatterns | No | Include patterns (using glob patterns, comma-separated) | |
| removeComments | No | Remove comments from the code | |
| removeEmptyLines | No | Remove empty lines from the code | |
| showLineNumbers | No | Show line numbers |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states it retrieves code as text. It lacks critical behavioral details: whether this is a read-only operation, potential performance impacts for large codebases, authentication needs, or output format specifics beyond 'single text output'.
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?
Single sentence, front-loaded with core purpose, zero wasted words. It efficiently conveys the essential action without unnecessary elaboration.
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?
For a complex tool with 9 parameters and no annotations or output schema, the description is inadequate. It doesn't explain the output structure, performance considerations, or error handling, leaving significant gaps for agent understanding.
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%, so the schema fully documents all 9 parameters. The description adds no parameter-specific information beyond implying retrieval scope ('entire codebase'), maintaining the baseline score 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 verb ('Retrieve') and resource ('entire codebase'), specifying it outputs as 'single text output using RepoMix'. It distinguishes from 'saveCodebase' (write vs. read) but not explicitly from 'getRemoteCodebase' (local vs. remote retrieval).
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?
No guidance on when to use this tool versus alternatives. It doesn't mention 'getRemoteCodebase' for remote codebases or 'saveCodebase' for saving output, leaving the agent to infer usage from tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
getRemoteCodebaseC
Retrieve a remote repository's codebase as a single text output using RepoMix
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Output format (xml, markdown, or plain) | xml |
| ignorePatterns | No | Ignore patterns (using glob patterns, comma-separated) | |
| includeDirectoryStructure | No | Include directory structure | |
| includeFileSummary | No | Include summary of each file | |
| includePatterns | No | Include patterns (using glob patterns, comma-separated) | |
| removeComments | No | Remove comments from the code | |
| removeEmptyLines | No | Remove empty lines from the code | |
| repo | Yes | GitHub repository URL or shorthand format (e.g., 'username/repo') | |
| showLineNumbers | No | Show line numbers |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but lacks critical behavioral details. It mentions output is 'a single text output' but doesn't disclose size limits, rate limits, authentication needs, error handling, or what 'RepoMix' entails. For a tool with 9 parameters and no annotations, this is insufficient.
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 a single, efficient sentence that front-loads the core purpose without wasted words. Every part earns its place by specifying retrieval, resource, output format, and method.
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?
For a complex tool with 9 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain the return structure, potential errors, or behavioral constraints, leaving significant gaps for the agent to operate effectively.
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%, so parameters are well-documented in the schema. The description adds no additional parameter semantics beyond implying the tool handles remote repositories via 'repo', which aligns with the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
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 action ('Retrieve') and resource ('remote repository's codebase'), specifying it returns 'a single text output using RepoMix'. It distinguishes from sibling 'getCodebase' by emphasizing 'remote' and 'RepoMix', but doesn't explicitly contrast with 'saveCodebase'.
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?
No explicit guidance on when to use this tool versus siblings 'getCodebase' or 'saveCodebase'. The description implies usage for remote repositories but doesn't specify alternatives or exclusions, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
saveCodebaseC
Save the codebase to a file using RepoMix
| Name | Required | Description | Default |
|---|---|---|---|
| cwd | No | Current working directory of the codebase (defaults to current dir) | |
| format | No | Output format (xml, markdown, or plain) | xml |
| ignorePatterns | No | Ignore patterns (using glob patterns, comma-separated) | |
| includeDirectoryStructure | No | Include directory structure | |
| includeFileSummary | No | Include summary of each file | |
| includePatterns | No | Include patterns (using glob patterns, comma-separated) | |
| outputFile | No | Output file path | repomix-output.txt |
| removeComments | No | Remove comments from the code | |
| removeEmptyLines | No | Remove empty lines from the code | |
| showLineNumbers | No | Show line numbers |
TDQS
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 states 'Save' which implies a write operation, but doesn't specify file system permissions, whether it overwrites existing files, error handling, or output format details. The mention of 'RepoMix' adds some context but lacks operational specifics needed for a mutation tool.
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 a single, efficient sentence that gets straight to the point. It uses minimal words to convey the core functionality without any fluff or redundant information. Every word earns its place.
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?
For a mutation tool with 10 parameters and no annotations or output schema, the description is insufficient. It doesn't explain what the tool returns, error conditions, or important behavioral aspects like file overwriting. The high parameter count and mutation nature require more contextual information than provided.
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 comprehensive parameter documentation. The description doesn't add any parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in description.
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 action ('Save') and resource ('codebase to a file') with the specific tool 'RepoMix'. It distinguishes from sibling tools 'getCodebase' and 'getRemoteCodebase' by indicating a save/write operation rather than retrieval. However, it doesn't specify what exactly gets saved (e.g., entire codebase, filtered content).
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?
No guidance is provided about when to use this tool versus alternatives. The description doesn't mention sibling tools or any contextual cues for choosing this over 'getCodebase' or 'getRemoteCodebase'. It's a standalone statement with no usage context.
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.
3 tool updates
v1.0.0- First observed
getCodebase - First observed
getRemoteCodebase - First observed
saveCodebase
TDQS
Scored across 3 tools
The three tools have distinct purposes: retrieving local codebase, retrieving remote codebase, and saving codebase. While 'getCodebase' and 'getRemoteCodebase' both retrieve code, the local/remote distinction is clear. There's minor potential confusion about whether 'saveCodebase' saves the local or remote version, but overall boundaries are well-defined.
All tools follow a consistent verb_noun pattern with camelCase styling: getCodebase, getRemoteCodebase, saveCodebase. The naming is predictable and follows the same grammatical structure throughout, making it easy for agents to understand the action-object relationship.
With only 3 tools, this feels quite minimal for a 'Codebase MCP' server. While the tools cover basic retrieval and saving operations, the scope seems limited - there are no tools for searching, analyzing, modifying, or managing codebase components. The count is borderline thin for what could be expected from a codebase management system.
For a codebase management server, there are significant gaps in functionality. The tools only provide retrieval and saving operations using RepoMix, but lack any tools for code analysis, search, modification, version control operations, or component management. This creates a dead-end surface where agents can only get and save entire codebases without any ability to work with code meaningfully.
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