
Codealive Mcp
io.github.CodeAlive-AI由 GitHub 帳號 CodeAlive-AI 發布v3.0.3更新於 Oct 24, 2026
Semantic code search and analysis from CodeAlive for AI assistants and agents.
概覽
CodeAlive 的 MCP 伺服器讓 AI 助理對已索引的程式碼儲存庫進行語意搜尋與結構化閱讀,從而在大型程式碼庫中精準定位並理解程式碼。
- 可以做什麼
- 列出已索引的儲存庫與工作區,取得單一儲存庫的整體導覽與有界檔案樹。以語意搜尋程式碼,或用字面、正規表達式比對文字與檔名,並回傳行級預覽。讀取儲存庫內相對路徑的檔案,可指定行範圍,並取得搜尋命中項目的完整原始碼。展開某個構件的呼叫圖、繼承與引用關係。檢視 ArtifactQuery 的欄位結構,並對選定儲存庫執行唯讀的中繼資料分析。在被明確要求時,對程式碼庫進行較慢、無狀態的綜合問答。
- 適用情境
- 適合助理在大型或不熟悉的程式碼庫中工作、需要精確的儲存庫脈絡而非盲目讀取檔案的情境;也適合在 CodeAlive 已索引的儲存庫上做語意與精確文字搜尋、關係查詢與中繼資料分析。
- 執行需求
- 需要 CodeAlive 帳號與 API 金鑰,透過環境變數 CODEALIVE_API_KEY 提供;儲存庫需已在 CodeAlive 中建立索引。清單中的套件以 Docker 在本機透過 stdio 執行;README 另說明了帶 Bearer 權杖或 OAuth 的遠端 HTTP 端點,以及 Python 3.11+ 的本機安裝方式。需要能連線至 CodeAlive 的網路。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Codealive Mcp,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
README
CodeAlive MCP: Deepest Context Engine for your projects (especially for large codebases)
Connect your AI assistant to CodeAlive's powerful code understanding platform in seconds!
This MCP (Model Context Protocol) server enables AI clients like Claude Code, Cursor, Claude Desktop, Continue, VS Code (GitHub Copilot), Cline, Codex, OpenCode, SourceCraft Code Assistant, SourceCraft CLI, Zed, KodaCode, GigaCode, Qwen Code, Gemini CLI, Roo Code, Goose, Kilo Code, Windsurf, Kiro, Qoder, n8n, and Amazon Q Developer to access CodeAlive's advanced semantic code search and codebase interaction features.
What is CodeAlive?
CodeAlive is a Context Engine for large codebases, powered by graph-based retrieval and exposed through MCP. It gives AI agents like Cursor, Claude Code, Codex, and other MCP-compatible tools precise repository context instead of forcing them to read files blindly. In our RepoQA benchmark, CodeAlive + Qwen3.6 deep reached frontier-agent quality at ~25x lower model cost, and semantic search reduced captured tokens by 45%.
It's like Context7, but for your (large) codebases.
It allows AI-Coding Agents to:
- Find relevant code faster with semantic search
- Understand the bigger picture beyond isolated files
- Provide better answers with full project context
- Reduce costs and time by removing guesswork
🛠 Available Tools
Once connected, you'll have access to these powerful tools:
get_data_sources- List your indexed repositories and workspacessemantic_search- Canonical semantic search across indexed artifactsgrep_search- Exact literal or regex text search inside file content, plus literal file-name/path matching (returns files likeForm.xmleven when their content never mentions the name), with line-level previews for content matchesget_repository_ontology- Get repository-level orientation for one selected repositoryget_file_tree- Inspect a bounded file tree for one repositoryread_file- Read a repository-relative file path, optionally with a line rangefetch_artifacts- Load the full source for relevant search hits (missing or inaccessible identifiers are reported back, not silently dropped)get_artifact_relationships- Expand call graph, inheritance, and reference relationships for one artifactget_artifact_query_schema- Inspect supported ArtifactQuery entities, fields, and examplesquery_artifact_metadata- Run read-only metadata analytics across selected repositorieschat- Stateless, slower synthesized codebase Q&A; call only when explicitly requested
🎯 Usage Examples
After setup, try these commands with your AI assistant:
- "Show me all available repositories" → Uses
get_data_sources - "Find authentication code in the user service" → Uses
semantic_search - "Find the exact regex that matches JWT tokens" → Uses
grep_search - "Explain how the payment flow works in this codebase" → Usually starts with
semantic_search/grep_search, then optionally useschat
semantic_search and grep_search should be the default tools for most agents. chat is a slower stateless synthesis fallback that can take substantially longer than retrieval, and is usually unnecessary when an agent can run a multi-step workflow with ontology, search, fetch/read, relationships, ArtifactQuery, and local file reads. If your agent supports subagents, the highest-confidence path is to delegate a focused subagent that orchestrates semantic_search and grep_search first.
📚 Agent Skill
For an even better experience, install the CodeAlive Agent Skill alongside the MCP server. The MCP server gives your agent access to CodeAlive's tools; the skill teaches it the best workflows and query patterns to use them effectively.
For most agents (Cursor, Copilot, Gemini CLI, Codex, and 30+ others) — install the skill:
For Claude Code — install the plugin (recommended), which includes the skill plus Claude-specific enhancements:
Table of Contents
- Agent Skill
- Quick Start (Remote)
- AI Client Integrations
- Advanced: Local Development
- Community Plugins
- HTTP Deployment (Self-Hosted & Cloud)
- Windows & WSL
- Available Tools
- Usage Examples
- Troubleshooting
- Publishing to MCP Registry
- License
🚀 Quick Start (Remote)
The fastest way to get started - no installation required! Our remote MCP server at https://mcp.codealive.ai/api provides instant access to CodeAlive's capabilities.
Step 1: Get Your API Key
- Sign up at https://app.codealive.ai/
- Navigate to MCP & API
- Click "+ Create API Key"
- Copy your API key immediately - you won't see it again!
Step 2: Open Your Client Guide
Choose your client in the MCP integration guides and follow the current setup instructions there.
🚀 Quick Start (Agentic Installation)
You may ask your AI agent to install the CodeAlive MCP server for you.
- Copy-paste the following prompt into your AI agent. Do not include your API key in the prompt:
Then allow execution.
- Restart your AI agent.
🤖 AI Client Integrations
Client-specific configuration is maintained in the CodeAlive documentation so file paths, transports, and authentication guidance stay current.
Start here: MCP integration guides
For an unlisted client, use these generic connection details and adapt them to the client's MCP configuration format:
- Endpoint:
https://mcp.codealive.ai/api - Transport: Streamable HTTP
- Authentication header:
Authorization: Bearer YOUR_API_KEY_HERE
For a private deployment, replace the endpoint with your server's /api URL. See Self-Hosting for deployment guidance.
Connecting the server is half the setup. Coding agents may continue using their built-in search unless project instructions tell them to prefer CodeAlive. Ready-made rules for
AGENTS.md,CLAUDE.md, and client-specific instruction files are in Instructing Coding Agents.
🔧 Advanced: Local Development
For developers who want to customize or contribute to the MCP server.
Prerequisites
- Python 3.11+
- uv (recommended) or pip
Installation
Local Server Configuration
After installing the server locally, point your MCP client at .venv/bin/python with src/codealive_mcp_server.py as the first argument and provide CODEALIVE_API_KEY in the process environment. Client-specific configuration belongs in the MCP integration guides.
Running HTTP Server Locally
HTTP transport validates Host and browser Origin headers. Loopback hosts
(localhost, 127.0.0.1, ::1) work without extra configuration. For a
shared hostname, configure an exact allowlist:
The equivalent repeatable CLI options are --allowed-host and
--allowed-origin. Do not use * for an Internet-facing server.
Testing Your Local Installation
After making changes, quickly verify everything works:
The smoke test verifies:
- Server starts and connects correctly
- All tools are registered
- Each tool responds appropriately
- Parameter validation works
- Runs in ~5 seconds
🌐 Community Plugins
🚢 HTTP Deployment (Self-Hosted & Cloud)
Deploy the MCP server as an HTTP service for team-wide access or integration with self-hosted CodeAlive instances.
Deployment Options
The CodeAlive MCP server can be deployed as an HTTP service using Docker. This allows multiple AI clients to connect to a single shared instance, and enables integration with self-hosted CodeAlive deployments.
Docker Compose (Recommended)
Create a docker-compose.yml file based on our example:
Configuration Options:
-
For CodeAlive Cloud (default):
- Remove
CODEALIVE_BASE_URLenvironment variable (uses defaulthttps://app.codealive.ai) - For remote clients with OAuth support, configure only
https://mcp.codealive.ai/apiand complete the browser sign-in when prompted - Existing API-key clients remain supported via
Authorization: Bearer YOUR_KEY
- Remove
-
For Self-Hosted CodeAlive:
- Set
CODEALIVE_BASE_URLto your CodeAlive instance URL (e.g.,https://codealive.yourcompany.com) - Set
CODEALIVE_MCP_ALLOWED_HOSTSto the exact hostname clients use for this MCP server - Clients must provide their API key via
Authorization: Bearer YOUR_KEYheader
- Set
See docker-compose.example.yml for the complete configuration template.
For example, current Codex and Claude Code clients can use browser OAuth without storing a CodeAlive API key:
Cursor and OpenCode also discover OAuth automatically from the same URL. Use
cursor-agent mcp login codealive or opencode mcp auth codealive when their UI does not prompt
automatically. API-key configuration remains available as a compatibility option.
OAuth 2.1 deployment profile
Remote HTTP deployments can enable browser authorization while keeping legacy API-key clients working during rollout. OAuth mode publishes MCP Protected Resource Metadata, validates exact issuer/resource-bound JWTs, and exchanges them for a separate short-lived Tool API token. The incoming MCP bearer token is never forwarded downstream.
The authorization server and MCP service values must match exactly. In CodeAlive Web.Server the
corresponding settings live under McpOAuth (Enabled, Issuer, Resource,
ToolApiResource, InternalClientId, and InternalClientSecret). Persist the Web.Server Data
Protection key ring and OpenIddict signing/encryption certificates across replicas and restarts.
For a zero-downtime internal credential rotation, give the new credential a new client ID, deploy
Web.Server with both current and PreviousInternalClientId/PreviousInternalClientSecret, roll
MCP replicas to the new current pair, then remove the previous pair. Web.Server deliberately fails
startup instead of changing a secret in place under an existing client ID.
Enable the Web.Server and MCP flags in the same rollout; a half-enabled deployment is not a valid
steady state. API-key credentials retain their explicit legacy grammar and are never used as a
fallback after OAuth validation fails.
Connecting MCP Clients to Your Deployed Instance
Use the same generic connection details as CodeAlive Cloud, replacing the endpoint with your deployment's /api URL:
- Endpoint:
https://your-server.example.com/api - Transport: Streamable HTTP
- Authentication header:
Authorization: Bearer YOUR_API_KEY_HERE
For the exact configuration format, open the relevant client integration guide.
🪟 Windows & WSL
Use the client-specific documentation for Windows and WSL setup:
For self-hosted servers running in WSL2, Windows clients must be able to reach the server's /api endpoint. Use mirrored networking on supported Windows 11 versions or connect through the WSL2 VM address.
🐞 Troubleshooting
Quick Diagnostics
-
Test the hosted service:
-
Check your API key:
-
Enable debug logging: Add
--debugto local server args
Common Issues
- "Connection refused" → Check internet connection
- "401 Unauthorized" → Verify your API key
- "No repositories found" → Check API key permissions in CodeAlive dashboard
- Client-specific logs → See your AI client's documentation for MCP logs
Windows / WSL Issues
docker: command not foundin WSL → Enable Docker Desktop WSL integration for your distro (Settings → Resources → WSL integration), or use the full path/usr/bin/dockerENOENTorspawn errorfornpx/python→ Non-interactive WSL shells don't inheritnvm/pyenvpaths. Use absolute paths in MCP configsConnection refusedto self-hosted server in WSL2 → WSL2 uses NAT networking;localhostdiffers between Windows and WSL2. Enable mirrored networking in.wslconfigor use the WSL2 VM IP (hostname -I)- Claude Desktop can't connect to WSL MCP server → Claude Desktop doesn't support WSL subprocess spawning. Use Remote HTTP (
https://mcp.codealive.ai/api), Docker Desktop, or thewsl.exeproxy pattern (see Windows & WSL section)
Getting Help
- 📧 Email: [email protected]
- 🐛 Issues: GitHub Issues
📦 Publishing to MCP Registry
For maintainers: see DEPLOYMENT.md for instructions on publishing new versions to the MCP Registry.
Privacy Policy
CodeAlive processes the repositories and queries you send through this extension in order to provide semantic search and codebase analysis. For complete privacy details, see CodeAlive Privacy Policy.
📄 License
MIT License - see LICENSE file for details.
Ready to supercharge your AI assistant with deep code understanding?
Get started now →
來源:README.md,提交 3755fef
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1- v3.0.3最新Sep 16, 2026
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