MCP Documentation Server

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 afull 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,.pdfsupport
- 📦 Published on theMCP Registry— installable via npx, no clone needed

{ "mcpServers": { "documentation": { "command": "npx", "args": ["-y", "@andrea9293/mcp-documentation-server"] } } }

Open your browser athttp://localhost:3080— the web UI starts automatically.

🤖 Agent Skill (REST API) — recommended for AI agents

Every MCP tool is also accessible via theREST APIonhttp://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.

curl -s http://127.0.0.1:3080/api/config curl -s http://127.0.0.1:3080/api/documents curl -s -X POST http://127.0.0.1:3080/api/search-all \ -H "Content-Type: application/json" \ -d '{"query": "your search", "limit": 5}'

A ready-to-use skill is included atskills/documentation-server/SKILL.md— it teaches your agent every endpoint with examples. Install it:

npx skills add https://github.com/andrea9293/mcp-documentation-server --skill documentation-server

- Add documents usingadd_documentor place.txt/.md/.pdffiles in the uploads folder and callprocess_uploads.
- Search across everything withsearch_all_documents, or within a single document withsearch_documents.
- Useget_context_windowto fetch neighboring chunks and give the LLM broader context.

The web interface starts automatically on port3080when 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 (ifGEMINI_API_KEYis set)
- 📁Upload Files— drag & drop files and process them into the knowledge base
- 🪟Context Window— explore chunks around a specific index

{ "mcpServers": { "documentation": { "command": "npx", "args": ["-y", "@andrea9293/mcp-documentation-server"] } } }

With environment variables (all optional)

{ "mcpServers": { "documentation": { "command": "npx", "args": ["-y", "@andrea9293/mcp-documentation-server"], "env": { "MCP_BASE_DIR": "/path/to/workspace", "GEMINI_API_KEY": "your-api-key-here", "MCP_EMBEDDING_MODEL": "Xenova/all-MiniLM-L6-v2", "START_WEB_UI": "true", "WEB_HOST": "127.0.0.1", "WEB_PORT": "3080" } } } }

All environment variables are optional. WithoutGEMINI_API_KEY, only the local embedding-based search tools are available.

The server registers the following tools (all validated with Zod schemas):

Configure via environment variables or a.envfile in the project root:

~/.mcp-documentation-server/ # Or custom path via MCP_BASE_DIR ├── data/ │ ├── orama-chunks.msp # Orama vector DB (child chunks + embeddings) │ ├── orama-docs.msp # Orama document DB (full content + metadata) │ ├── orama-parents.msp # Orama parent chunks DB (context sections) │ ├── migration-complete.flag # Written after legacy JSON migration │ └── *.md # Markdown copies of documents └── uploads/ # Drop .txt, .md, .pdf files here

Models are downloaded on first use (~80–420 MB). The vector dimension is determined automatically from the provider.

⚠️Important: Changing the embedding model requires re-adding all documents — embeddings from different models are incompatible. The Orama database is recreated automatically when the dimension changes.

Server (FastMCP, stdio) ├─ Web UI (Express, port 3080) │ └─ REST API → DocumentManager └─ MCP Tools └─ DocumentManager ├─ OramaStore — Orama vector DB (chunks DB + docs DB + parents DB), persistence, migration ├─ IntelligentChunker — Parent-child chunking (code, markdown, text, PDF) ├─ EmbeddingProvider — Local embeddings via @xenova/transformers │ └─ EmbeddingCache — LRU in-memory cache └─ GeminiSearchService — Optional AI search via Google Gemini

- OramaStoremanages three Orama instances: one for document metadata/content, one for child chunks with vector embeddings, and one for parent chunks (context sections). All are persisted to binary files on disk and restored on startup.
- IntelligentChunkerimplements the Parent-Child Chunking pattern: documents are first split into large parent chunks that preserve full context (sections, paragraphs), then each parent is further split into small child chunks for precise vector search. At query time, results are deduplicated by parent so that the LLM receives both the matched fragment and the broader context.
- EmbeddingProviderlazily loads a Transformers.js model for local inference — no API calls needed.

git clone https://github.com/andrea9293/mcp-documentation-server.git cd mcp-documentation-server npm install

npm run dev # FastMCP dev mode with hot reload npm run build # TypeScript compilation npm run inspect # FastMCP web UI for interactive tool testing npm start # Direct tsx execution (MCP server + web UI) npm run web # Run only the web UI (development) npm run web:build # Run only the web UI (compiled)

- Fork the repository
- Create a feature branch:git checkout -b feature/name
- FollowConventional Commitsfor messages
- Open a pull request

- 📖Documentation
- 🐛Report Issues
- 💬MCP Community
- 🤖Google AI Studio— get a Gemini API key

Built withFastMCP,Orama, and TypeScript

A knowledge base server that processes local documents (PDF, DOCX, TXT, HTML) and answers questions based on their content using similarity search.