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mcp-knowledge-server

by NavinAnik
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Python
Hybrid

MCP Knowledge Server

Production-grade Retrieval-Augmented Generation (RAG) server with centralized knowledge base backed by Qdrant. Exposes identical functionality through Model Context Protocol (MCP) and FastAPI REST API.

Designed for enterprise scale: clean architecture, SOLID principles, dependency injection, and fully env-configurable providers.

Architecture

Cursor IDE (MCP Client)
        ↓
   MCP Server (stdio / streamable-HTTP)
        ↓
   Application Services
        ↓
   RAG Pipeline → Embedding → Qdrant
        ↓
   PostgreSQL (metadata) + Knowledge Base

Key Components

Layer

Responsibility

app/api/

FastAPI REST endpoints

app/mcp/

MCP tool registration (12 tools)

app/services/

Use case orchestration

app/rag/

Retrieval pipeline, prompts, compression

app/domain/

Entities, ports, exceptions

app/infrastructure/

LLM, embeddings, Qdrant, persistence

app/ingestion/

Loaders, chunkers, cleaning

Related MCP server: estudIA-MCP

Quick Start

Prerequisites

  • Python 3.12+

  • uv package manager

  • Qdrant (local or Docker)

  • Ollama (optional, for local LLM)

Local Development

# Clone and setup
git clone <repo-url> mcp-knowledge-server
cd mcp-knowledge-server
cp .env.example .env

# Install dependencies
./scripts/setup.sh

# Start Qdrant (Docker)
docker run -p 6333:6333 qdrant/qdrant:v1.12.5

# Start REST API
uv run mcp-knowledge-server-api

# Start MCP server (stdio for Cursor)
uv run mcp-knowledge-server-mcp-stdio

Docker Compose (Full Stack)

cp .env.example .env
docker compose -f docker/docker-compose.yml up

Services:

Configuration

All settings via .env — never modify source code to change providers.

LLM Providers

# Local (macOS)
LLM_PROVIDER=ollama
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_MODEL=qwen3:8b

# Cloud
LLM_PROVIDER=openai
OPENAI_API_KEY=sk-...
OPENAI_MODEL=gpt-4.1

Supported LLM providers: openai, anthropic, gemini, groq, together, openrouter, ollama, lmstudio, llamacpp, openai_compatible

Embedding Providers

EMBEDDING_PROVIDER=sentence_transformers
EMBEDDING_MODEL=all-MiniLM-L6-v2

Supported: sentence_transformers, openai, ollama, voyage, cohere

Chunking Strategies

CHUNK_STRATEGY=recursive  # recursive | token | markdown | semantic
CHUNK_SIZE=1000
CHUNK_OVERLAP=200

macOS + Ollama Setup

# Install Ollama
brew install ollama

# Start Ollama
ollama serve

# Pull recommended models
ollama pull qwen3:8b
ollama pull nomic-embed-text

# Configure .env
LLM_PROVIDER=ollama
OLLAMA_MODEL=qwen3:8b
EMBEDDING_PROVIDER=ollama
OLLAMA_EMBEDDING_MODEL=nomic-embed-text

Recommended models: qwen3, qwen2.5, llama3.2, mistral, gemma3, deepseek, phi

Cursor MCP Configuration

Copy .cursor/mcp.json.example to your Cursor MCP settings:

{
  "mcpServers": {
    "knowledge-server": {
      "command": "uv",
      "args": ["run", "python", "-m", "app.mcp.main"],
      "cwd": "/path/to/mcp-knowledge-server",
      "env": {
        "LLM_PROVIDER": "ollama",
        "OLLAMA_BASE_URL": "http://localhost:11434",
        "OLLAMA_MODEL": "qwen3:8b"
      }
    }
  }
}

MCP Tools

Tool

Description

search_documents

Semantic search over knowledge base

rag_answer

Generate RAG answer with citations

add_document

Ingest a document file

update_document

Re-ingest an existing document

delete_document

Remove document and vectors

list_documents

List indexed documents

get_document

Get document metadata

similar_documents

Find similar chunks

create_collection

Create a new collection

delete_collection

Delete a collection

list_collections

List all collections

health_check

Server health status

REST API

OpenAPI docs: http://localhost:8000/docs

Examples

# Health check
curl http://localhost:8000/health

# Upload document
curl -X POST http://localhost:8000/documents/upload \
  -F "file=@documents/sample.txt" \
  -F "collection=knowledge_base"

# Search
curl -X POST http://localhost:8000/search \
  -H "Content-Type: application/json" \
  -d '{"query": "What is this about?", "top_k": 5}'

# RAG answer
curl -X POST http://localhost:8000/rag \
  -H "Content-Type: application/json" \
  -d '{"query": "Summarize the knowledge base"}'

# List collections
curl http://localhost:8000/collections

# Create collection
curl -X POST http://localhost:8000/collections \
  -H "Content-Type: application/json" \
  -d '{"name": "my_docs", "description": "My documents"}'

Supported Document Formats

PDF, DOCX, TXT, Markdown, HTML, CSV

Adding New Providers

LLM Provider

  1. If OpenAI-compatible: add config to OpenAICompatibleLLMProvider.from_settings() in app/infrastructure/llm/openai_compatible.py

  2. If custom API: implement BaseLLMProvider in app/infrastructure/llm/

  3. Register in LLMProviderFactory in app/infrastructure/llm/factory.py

  4. Add env vars to .env.example

Document Loader

  1. Implement BaseDocumentLoader in app/ingestion/loaders/

  2. Register in LoaderRegistry in app/ingestion/loaders/registry.py

Embedding Provider

  1. Implement BaseEmbeddingProvider in app/infrastructure/embeddings/

  2. Register in EmbeddingProviderFactory

Development

# Install with dev dependencies
uv sync --all-extras

# Lint
uv run ruff check app tests
uv run black --check app tests

# Type check
uv run mypy app

# Tests
uv run pytest

# Pre-commit
uv run pre-commit install
uv run pre-commit run --all-files

Project Structure

app/
├── api/           # FastAPI REST API
├── mcp/           # MCP server tools
├── services/      # Application use cases
├── rag/           # RAG pipeline stages
├── domain/        # Entities, ports, exceptions
├── infrastructure/# External adapters
├── ingestion/     # Loaders, chunkers, cleaning
├── config/        # Pydantic settings
├── logging/       # Structured logging
└── container.py   # Composition root (DI)
tests/
docker/
scripts/
alembic/

Deployment

Production Checklist

  • Set APP_ENV=production

  • Use PostgreSQL: DATABASE_URL=postgresql+asyncpg://...

  • Configure Qdrant with API key and HTTPS

  • Enable auth: ENABLE_AUTH=true, set API_KEY

  • Use cloud LLM or dedicated Ollama instance

  • Run behind reverse proxy (nginx/traefik)

  • Set resource limits in Docker Compose

Environment Variables

See .env.example for the complete list.

License

MIT

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