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MCP ( MCP ) Python server to use with continue.dev

@alexsmirnov

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alexsmirnov

关于 MCP ( MCP ) Python server to use with continue.dev

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MCP ( MCP ) Python server to use with continue.dev 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-server-continue": {
      "command": "uv",
      "args": [
        "run",
        "--project",
        "<local",
        "copy>",
        "mcps",
        "--vault",
        "<Vault",
        "Folder>",
        "--reindex"
      ]
    }
  }
}

MCP ( MCP ) Python server to use with continue.dev 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 Model Context Protocol ( MCP ) Python server to use with continue.dev?

This MCP server provides access to an Obsidian.md vault with search and read tools, as well as a web deep research tool similar to Perplexity.ai. It is designed for AI agents and knowledge retrieval, and can be used as a shared knowledge base and memory across projects.

How to use Model Context Protocol ( MCP ) Python server to use with continue.dev?

Clone the repository, create a .env file from env.example, then run uv run --project <local copy> mcps --vault <Vault Folder> --reindex to index the vault. Start the server with uv run --project <local copy> mcps --vault <Vault Folder> --port 1234. It runs as an HTTP MCP server.

Key features of Model Context Protocol ( MCP ) Python server to use with continue.dev

  • Web deep research agent using iterative search and content extraction
  • Hybrid vector + BM25 search for Obsidian vault notes
  • Note chunking by headers with frontmatter metadata (title, description, tags)
  • Summary chunk generated by LLM for improved recall
  • Reranking via Cohere/Voiage.ai API or Reciprocal Rank Fusion
  • Tag and file path filtering for narrowed searches

Use cases of Model Context Protocol ( MCP ) Python server to use with continue.dev

  • AI coding agents (Claude Code, Cursor) retrieving relevant notes from a large Obsidian vault
  • Answering technical or academic questions via web research, then storing results in the vault
  • Shared knowledge base accessible by multiple AI tools across different projects

常见问题

1

What LLM provider does the server require?

It uses a single LLM API provider. The author uses LiteLLM Gateway, but Openrouter also works.

2

Where are the indexed notes and embeddings stored?

Indexed chunks, metadata, and vector embeddings are stored in a local database. The original vault remains in its folder.

3

What transport does the server use?

The server runs as an HTTP MCP server on a configurable port (e.g., 1234). This allows a single instance to be shared across multiple AI tools.

4

What are the limitations of the web research tool?

It does not attempt to bypass bot protections, so some public sources may be inaccessible. It is optimized for technical/academic content.

5

How does the search handle note format?

The server assumes a specific note structure: frontmatter with `title`, `description`, `tags`, content split by headers, wikilinks, and notes no longer than 200 lines. It works best with vaults following this convention.

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