What are the runtime dependencies?
Ragdocs requires Node.js, a Qdrant vector database (local or cloud), and an embeddings provider – either a local Ollama instance or an OpenAI API key.
M
MCP Server Ragdocs
@sanderkooger
基本信息
关于 MCP Server Ragdocs
An MCP server that provides tools for retrieving and processing documentation through vector search, both locally or hosted. Enabling AI assistants to augment their responses with relevant documentation context.
MCP Server Ragdocs 配置
MCP Server Ragdocs 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 Ragdocs?
Ragdocs is an MCP server that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
How to use Ragdocs?
Install and run it via npx -y @sanderkooger/mcp-server-ragdocs. Configure the server with environment variables for your embeddings provider (Ollama or OpenAI) and Qdrant vector database. Add the configuration to your MCP host (e.g., Claude Desktop) using the provided JSON snippets.
Key features of Ragdocs
- Vector-based documentation search and retrieval
- Support for multiple documentation sources
- Local (Ollama) or OpenAI embedding generation
- Automated documentation processing and indexing
- Real-time context augmentation for LLMs
Use cases of Ragdocs
- Enhancing AI responses with relevant documentation
- Building documentation-aware AI assistants
- Creating context-aware tooling for developers
- Implementing semantic documentation search
- Augmenting existing knowledge bases
常见问题
How can I deploy Ragdocs locally or in the cloud?
For local development, use the provided Docker Compose file to start Qdrant and Ollama. For production, use a hosted Qdrant Cloud service and set `QDRANT_URL` and `QDRANT_API_KEY`.
Which embeddings providers are supported?
Ragdocs supports Ollama (using the `nomic-embed-text` model) and OpenAI as embeddings providers, configured via the `EMBEDDINGS_PROVIDER` environment variable.
How do I use Ragdocs with Claude Desktop?
Add a JSON entry under `mcpServers` in your `claude_desktop_config.json`, specifying the command, arguments, and environment variables for your chosen provider and Qdrant instance.
What tools does Ragdocs expose?
It provides `search_documentation`, `list_sources`, `extract_urls`, `remove_documentation`, `list_queue`, `run_queue`, and `clear_queue` for managing and querying documentation.
记忆与知识 分类下的更多 MCP 服务器
PLUR
plur-aiAI agents start every session with amnesia — you re-explain the project, repeat your preferences, and correct the same mistakes over and over. PLUR gives them a memory that persists. Your agent's corrections, preference
226
Agimem
Persistent memory for AI agents. Hosted MCP server with capsule-scoped key/value storage, tags, TTL expiration, bulk ops, and RBAC.
HandbookHub MCP
veryayskiySearch saved employee handbooks from your AI assistant. Four read-only tools list authorized handbooks, browse policies, search content, and read source pages with citation links. Uses scoped bearer tokens and Streamable
Sooveryn
PANDAPROGSooveryn gives Claude Code a team of AI personas that remembers your project. Soov, the partner and team lead, keeps your decisions and how you work in a lasting memory, and finds them again by words and by meaning, from
Memory
modelcontextprotocolModel Context Protocol Servers
88.1K
评论