Local Document Search & Summarization

MCP Documentation Server

A TypeScript-based Model Context Protocol (MCP) server that provides local-first document management and semantic search. Documents are stored in an embedded Orama vector database with hybrid search (full-text + vector), intelligent chunking, and local AI embeddings - no external database or cloud service required.

Core capabilities

🌐 Web UI

• Built-in Web Interface: A full-featured web dashboard starts automatically alongside the MCP server - no additional setup required
• Complete Tool Coverage: Every MCP tool is accessible from the browser: add/view/delete documents, semantic search, AI search, file uploads, and context window exploration
• Drag & Drop Uploads: Upload .txt, .md, and .pdf files directly from the browser
• Configurable: Disable with START_WEB_UI=false or change the port with WEB_PORT

🔍 Search & Intelligence

• Hybrid Search: Combined full-text and vector similarity powered by Orama, for both single-document and cross-document queries
• AI-Powered Search 🤖: Advanced document analysis with Google Gemini AI for contextual understanding and intelligent insights (optional, requires API key)
• Context Window Retrieval: Fetch surrounding chunks to provide LLMs with richer context

⚡ Performance & Architecture

• Orama Vector DB: Embedded search engine with zero native dependencies - replaces manual JSON storage and cosine similarity
• LRU Embedding Cache: Avoids recomputing embeddings for repeated content and queries
• Parent-Child Chunking: Documents are split into large context-preserving parent chunks and small precise child chunks for vector search - search results include both the matched fragment and the full surrounding context
• Streaming File Reader: Handles large files without high memory usage
• Automatic Migration: Legacy JSON documents are migrated to Orama on first startup - no manual intervention needed

📁 File Management

• Upload processing: Drop .txt, .md, or .pdf files into the uploads folder and process them with a single tool call
• Copy-based storage: Original files are backed up alongside the database
• Local-only storage: All data resides in ~/.mcp-documentation-server/

Quick Start

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.

🤖 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 with the server from AI agents (Claude Code, OpenCode, Gemini CLI, Cursor, etc.) because it avoids loading MCP tool schemas into the conversation context - only the response JSON enters

# Check if the server is running
curl -s http://127.0.0.1:3080/api/config

# List all documents
curl -s http://127.0.0.1:3080/api/documents

# Search across all 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 at skills/documentation-server/SKILL.md - it teaches your agent every endpoint with examples. Install it:

# Install from the public repo
npx skills add https://github.com/andrea9293/mcp-documentation-server --skill documentation-server

Configure the mcp only if you want a granular control about environment variables

Web UI

The web interface starts automatically on port 3080 when the MCP server launches. Open your browser at:

http://localhost:3080

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

Example configuration for an MCP client (e.g., Claude Desktop, VS Code):

Quick Start

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

Advanced with env vars (all vars are 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. 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
list_uploads_files	Lists files in the uploads folder with size and format info
get_ui_url	Returns the Web UI URL (e.g. http://localhost:3080) - useful to open the dashboard or to locate the uploads folder from the browser

🔍 Search

Tool	Description
search_documents	Semantic vector search within a specific document
search_all_documents	Hybrid (full-text + vector) cross-document search
get_context_window	Returns a window of chunks around a given chunk index
search_documents_with_ai	🤖 AI-powered search using Gemini (requires GEMINI_API_KEY)

Configuration

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

Variable	Default	Description
MCP_BASE_DIR	~/.mcp-documentation-server	Base directory for data storage
MCP_EMBEDDING_MODEL	Xenova/all-MiniLM-L6-v2	Embedding model name
GEMINI_API_KEY	-	Google Gemini API key (enables search_documents_with_ai)
MCP_CACHE_ENABLED	true	Enable/disable LRU embedding cache
START_WEB_UI	true	Set to false to disable the built-in web interface
WEB_HOST	127.0.0.1	Bind address for the web UI (use 0.0.0.0 to expose on all interfaces)
WEB_PORT	3080	Port for the web UI
MCP_STREAMING_ENABLED	true	Enable streaming reads for large files
MCP_STREAM_CHUNK_SIZE	65536	Streaming buffer size in bytes (64KB)
MCP_STREAM_FILE_SIZE_LIMIT	10485760	Threshold to switch to streaming (10MB)

Storage layout

~/.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

Embedding Models

Set via MCP_EMBEDDING_MODEL:

Model	Dimensions	Notes
Xenova/all-MiniLM-L6-v2	384	Default - fast, good quality
Xenova/paraphrase-multilingual-mpnet-base-v2	768	Recommended - best quality, multilingual

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.

Architecture

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

• OramaStore manages 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.
• IntelligentChunker implements 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.
• EmbeddingProvider lazily loads a Transformers.js model for local inference - no API calls needed.

Development

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)

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Built with FastMCP, Orama, and TypeScript