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

@alexsmirnov

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

alexsmirnov

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

暂无概览

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

What LLM provider does the server require?

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

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.

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.

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.

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.

其他分类下的更多 MCP 服务器

Health by Dr Brad

Dr Brad Stanfield

Health by Dr Brad works out a health plan from your own results: which screening is due at your age, and evidence-based suggestions, each with the reason and the published guideline or study behind it. Your record is one

Next Generation Movers: free moving quotes (remote MCP)

Next Generation Movers

Free moving quotes for AI agents from Next Generation Movers, a licensed interstate mover (USDOT 3764697). US long-distance nationwide and local DC/MD/VA. Indicative ballpark price and booking options; optional $0.01 x40

MaxKB

1Panel-dev

🔥 MaxKB is an open-source platform for building enterprise-grade agents. 强大易用的开源企业级智能体平台。

22.0K

Screenpipe

screenpipe

YC (S26) | Open Computer History | Continuously record your company computer work, map your workflows, help you find work worth automating, and power your agents' context