Codebase MCP
コードベース MCP
RepoMixを使用してコードベース全体を取得および分析するためのツールを提供するモデルコンテキストプロトコル (MCP)サーバー実装。
この MCP により、Cursor の Composer Agent などの AI エージェントはコードベース全体を一度に自動的に読み取って理解できるようになり、開発者は大規模なコードベースでの作業が容易になり、AI アシスタントはプロジェクトの包括的なコンテキストを把握できるようになります。
特徴
📚コードベースの取得: コードベース全体を単一のテキスト出力としてさまざまな形式 (XML、Markdown、Plain) で取得します。
🌐リモートリポジトリのサポート:リモートGitHubリポジトリを直接処理します
💾ファイル保存: 処理されたコードベースをファイルに保存します
🔧カスタマイズ可能なオプション: さまざまなオプション (コメント、行番号、ファイルの概要など) を使用して、コードベースの処理方法を制御します。
Related MCP server: CodeAlive MCP
インストール
NPMから(推奨)
# Install the package globally
npm install -g codebase-mcp
# Install RepoMix (required dependency)
codebase-mcp installGitHubから
# 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カーソルとの統合
この MCP を Cursor の Composer Agent で使用するには:
オープンカーソルIDE
サイドバーのComposerアイコンをクリックします
上部の「MCPサーバー」ボタンをクリックします
「新しいMCPサーバーを追加」をクリックします
詳細を入力してください:
名前:
Codebase MCP(または任意の名前)タイプ:
commandコマンド:
codebase-mcp start
「追加」をクリックして保存します
MCP を追加すると、Composer Agent に 3 つの強力なツールが提供されます。
利用可能なツール
コードベースを取得する
目的: 現在のワークスペース/プロジェクトを分析する
使用する場合: AIにコードベース全体を理解させたい場合
プロンプトの例:「コードベースを分析して構造を理解してください」
リモートコードベースを取得する
目的: 公開されているGitHubリポジトリを取得して分析する
使用する場合: 他のプロジェクトを探索したり理解したりしたい場合
プロンプトの例:「github.com/username/repo のリポジトリを分析できますか?」
コードベースを保存
目的: コードベース分析を後で使用するためにファイルに保存します
使用する場合: コードベースのスナップショットを保存したり共有したりしたい場合
プロンプトの例:「このコードベースの分析を保存して、後で確認します」
カーソルでの使用例
Composer Agent で使用できるプロンプトの例を次に示します。
"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 は、リクエストに応じて適切なツールを自動的に使用します。
カーソル外での使用
MCPサーバーの起動
codebase-mcp startこれにより、MCP サーバーが stdio モードで起動され、MCP 互換のクライアントであればどれでも使用できるようになります。
ライセンス
マサチューセッツ工科大学
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. Dates show when Glama detected each change.
3 tool updates
v1.0.0- First observed
getCodebase - First observed
getRemoteCodebase - First observed
saveCodebase
TDQS
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.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
An MCP server that gives your AI access to the source code and docs of all public github repos
Code intelligence for LLMs. Analyze, search, and retrieve code from any public git repository.
The Cortex MCP server provides read-only access to real-time engineering context from the Cortex developer portal, allowing AI coding assistants to answer natural language questions about your organization's catalog (microservices, libraries, domains, teams, infrastructure), scorecards (engineering standards and best practices), initiatives (goals and deadlines), and Engineering Intelligence metrics. It includes tools for querying documentation, tracking personal entities, and accessing AI-assisted insights across the entire Cortex ecosystem.
Related MCP Servers
- AlicenseAqualityAmaintenanceA Model Context Protocol (MCP) server that helps large language models index, search, and analyze code repositories with minimal setup141,005MIT

CodeAlive MCPofficial
AlicenseNot gradedqualityAmaintenanceA Model Context Protocol server that enhances AI agents by providing deep semantic understanding of codebases, enabling more intelligent interactions through advanced code search and contextual awareness.89MIT- AlicenseAqualityDmaintenanceAn enterprise-grade Model Context Protocol server that provides comprehensive GitHub repository analysis and AI-powered development assistance through 11 streamlined tools.112MIT
- FlicenseBqualityDmaintenanceA Model Context Protocol (MCP) server that enables AI applications to access and analyze local code repositories without manual uploads, providing file listing, content reading, code searching, and project structure analysis capabilities.7-
Appeared in Searches
- Code intelligence tools for LLMs to understand codebase structure and locate functionality
- A tool for reading, understanding, and summarizing code in GitHub repositories
- A tool for managing entire project code context
- MCP server for contextual codebase analysis and task-focused code understanding
- MCP server for code understanding and agent context initialization
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/DeDeveloper23/codebase-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server