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andrea9293/mcp-documentation-server

andrea9293/mcp-documentation-server

Document management with Gemini
在 GitHub 查看 ↗直链 · 无中转页
来源明细
收录自 abordage/awesome-mcp 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-14 · 最近见到 2026-10-16 · 共见到 4 次 · 被 3 个来源列出
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综合分 303 · 3 个来源 · mcp
常见问题(答案都来自我们的记录)
怎么安装?
我们【没有】从该仓库取到安装命令(我们的 install 覆盖率只有约 4%)。请看它的 README 或 releases 页。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
我们收录于 2026-10-14;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

MCP Documentation Server

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

**Local-first document management and semantic search for AI coding agents.** No external databases, no cloud APIs, no vendor lock-in.
Unlike other MCP servers that are CLI-only, this one ships with a **full web dashboard** — browse, search, upload, and manage your knowledge base from your browser. Every MCP tool is also exposed as a REST API, giving AI agents a lean, schema-free interface.
- **🏠 Runs fully offline** — Orama vector DB with local AI embeddings (Transformers.js)
- **🌐 Built-in Web UI** — starts automatically on port 3080 alongside the MCP server
- **🔍 Hybrid search** — full-text + vector similarity with parent-child chunking
- **🤖 Optional AI search** — Google Gemini for advanced document analysis (bring your own key)
- **📁 Drag & drop uploads** — .txt, .md, .pdf support
- **📦 Published on the MCP Registry** — installable via npx, no clone needed
Quick Start
Open your browser at http://localhost:3080 — the web UI starts automatically.
🤖 Agent Skill (REST API) — recommended for AI agents
Every MCP tool is also accessible via the **REST API** on http://127.0.0.1:3080/api/. This is the recommended way to interact from AI agents (Claude Code, OpenCode, Gemini CLI, Cursor) because it avoids loading MCP tool schemas into the conversation context — only the response JSON enters.
A ready-to-use skill is included at skills/documentation-server/SKILL.md — it teaches your agent every endpoint with examples. Install it:
Basic workflow
1. Add documents using add_document or place .txt / .md / .pdf files in the uploads folder and call process_uploads.
2. Search across everything with search_all_documents, or within a single document with search_documents.
3. Use get_context_window to fetch neighboring chunks and give the LLM broader context.
Web UI
The web interface starts automatically on port **3080** when the MCP server launches. From the web UI you can:
- 📊 **Dashboard** — overview of all documents and stats
- 📄 **Documents** — browse, view, and delete documents
- ➕ **Add Document** — create documents with title, content, and metadata
- 🔍 **Search All** — semantic search across all documents
- 🎯 **Search in Doc** — search within a specific document
- 🤖 **AI Search** — Gemini-powered analysis (if GEMINI_API_KEY is set)
- 📁 **Upload Files** — drag & drop files and process them into the knowledge base
- 🪟 **Context Window** — explore chunks around a specific index
Configure an MCP client
Minimal
With environment variables (all optional)
All environment variables are optional. Without GEMINI_API_KEY, only the local embedding-based search tools are available.
MCP Tools
The server registers the following tools (all validated with Zod schemas):
📄 Document Management
| Tool | Description |
| --- | --- |
| add_document | Add a document (title, content, optional metadata) |
| list_documents | List all documents with metadata and content preview |
| get_document | Retrieve the full content of a document by ID |
| delete_document | Remove a document, its chunks, database entries, and associated files |
📁 File Processing
| Tool | Description |
| --- | --- |
| process_uploads | Process all files in the uploads folder (chunking + embeddings) |
| get_uploads_path | Returns the absolute path to the uploads folder |
来源
awesome · https://github.com/andrea9293/mcp-documentation-server
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