Power your agent
पब्लिशर की अपनी कमांड। PlugMCP एंडपॉइंट नहीं।
Host a copy with our keys and logs.
npx -y @andrea9293/[email protected]Claude Code में लोकल कमांड के लिए इंस्टॉल लिंक नहीं है। नीचे दी गई कमांड कॉपी करें और टर्मिनल में चलाएँ।
claude mcp add io.github.andrea9293-mcp-documentation-server -- npx -y @andrea9293/mcp-documentation-server@1.15.3टर्मिनल में चलाएँ।
{ "mcpServers": { "io.github.andrea9293-mcp-documentation-server": { "command": "npx", "args": [ "-y", "@andrea9293/[email protected]" ] } }}claude_desktop_config.json
रिमोट URL या आपकी मशीन पर कमांड? दोनों इंस्टॉल में क्या अंतर है
PlugMCP पर चलाएँ
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
लॉन्च
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.
.txt, .md, .pdf support{
"mcpServers": {
"documentation": {
"command": "npx",
"args": ["-y", "@andrea9293/mcp-documentation-server"]
}
}
}
Open your browser at http://localhost:3080 — the web UI starts automatically.
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.
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 at skills/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_document or place .txt / .md / .pdf files in the uploads folder and call process_uploads.search_all_documents, or within a single document with search_documents.get_context_window to fetch neighboring chunks and give the LLM broader context.The web interface starts automatically on port 3080 when the MCP server launches. From the web UI you can:
GEMINI_API_KEY is set){
"mcpServers": {
"documentation": {
"command": "npx",
"args": ["-y", "@andrea9293/mcp-documentation-server"]
}
}
}
{
"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.
The server registers the following tools (all validated with Zod schemas):
Configure via environment variables or a .env file 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
Set via MCP_EMBEDDING_MODEL:
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
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)
git checkout -b feature/nameMIT — see LICENSE
Local-first document management and semantic search for AI coding agents
{ "mcpServers": { "io.github.andrea9293-mcp-documentation-server": { "command": "npx", "args": [ "-y", "@andrea9293/[email protected]" ] } }}टूल
अभी टूल सूची नहीं है। रिमोट सर्वर को पहले हेल्थ चेक के बाद सूची मिलती है।
FAQ
इस पेज पर Cursor, Claude Desktop, Claude Code या VS Code के लिए stdio कॉन्फ़िग कॉपी करें। यह पब्लिशर का इंस्टॉल इस्तेमाल करता है, PlugMCP प्रॉक्सी नहीं।
इस रिकॉर्ड में कोई ज़रूरी एनवायरनमेंट वेरिएबल दर्ज नहीं है।
MCP Documentation Server stdio पर लोकल चलता है।
पिछली जाँच 2026-11-09 को हुई। Local-first document management and semantic search for AI coding agents