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Contexo MCP Server

@sugihAF

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sugihAF

提交者

Sugih AF

关于 Contexo MCP Server

AI Agent Context Versioning and Synchronization

#ai#llm#context

Contexo MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "contexo": {
      "command": "ctx",
      "args": [
        "mcp"
      ]
    }
  }
}

Contexo MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Contexo?

Contexo is an open-source MCP server that gives AI coding agents a persistent, shared memory of your project. Instead of re‑explaining architecture, conventions, and past decisions each session, agents pull what’s already known before starting work and push back what they learn afterward. Knowledge is stored in a git‑backed repository, making it versioned, diffable, reviewable, and shareable across teammates and tools—and because it’s built on MCP, the same knowledge can be used with Claude Code, Cursor, Codex, and other compatible agents.

How to use Contexo?

Contexo is available as an MIT‑licensed Go CLI and server that you can self‑host, plus an optional hosted version. Install the CLI and run the server to start capturing and sharing project context with your AI coding agents. The README does not provide specific installation or configuration commands.

Key features of Contexo

  • Persistent, versioned project memory stored in git
  • Section‑aware diffs and full history
  • Drift detection to flag outdated context
  • Agent‑assisted merges for collaborative updates
  • Structured capture of the why behind decisions
  • Cross‑agent compatibility via the MCP protocol

Use cases of Contexo

  • Avoid repeating architecture and conventions in every new AI session
  • Share project decisions, gotchas, and reasoning across a team
  • Keep context in sync between different AI coding tools (e.g., Claude Code and Cursor)
  • Track how project knowledge evolves over time with diffs and history

常见问题

1

What problem does Contexo solve?

Contexo eliminates the “context tax” by giving AI agents a shared memory of your project, so they don’t need to re‑learn architecture, conventions, or past decisions every session.

2

How is Contexo different from storing context in `CLAUDE.md` or vendor‑specific memory?

Contexo centralizes context in a git‑backed repository that is versioned, diffable, reviewable, and shareable across teammates and tools, rather than scattering it in local files or vendor‑specific features.

3

Is Contexo open source and can I self‑host it?

Yes. Contexo is open‑core: the Go CLI and server are MIT‑licensed and can be self‑hosted. An optional hosted version is also available.

4

Which AI coding agents work with Contexo?

Contexo works with any MCP‑compatible agent, including Claude Code, Cursor, Codex, and others.

5

Where does project knowledge actually live?

Knowledge lives in a git‑backed repository, so it is versioned, diffs are visible, and the history is reviewable alongside your codebase.

评论

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