DuckDuckGo MCP Agent

A minimal, production-ready Model Context Protocol (MCP) server enabling LLMs to perform web searches via DuckDuckGo. Built with FastAPI and Server-Sent Events (SSE).

Features:

• ✅ Single-file implementation (~324 lines)

• ✅ No API keys required

• ✅ HTTP + SSE streaming (LM Studio compatible)

• ✅ Docker-ready

• ✅ Minimal dependencies (FastAPI, Uvicorn, duckduckgo-search)

Quick Start

Prerequisites

• Python 3.10+ or Docker

• LM Studio (optional)

Option 1: Dev Container (Recommended)

1. Install VS Code and Docker Desktop

2. Install Dev Containers extension

3. Open folder in VS Code → "Reopen in Container"

Option 2: Local Development

make install-dev
make run-dev
# Server: http://localhost:8000

Option 3: Docker

make docker-build
make docker-run
# Or: make docker-compose

Usage

HTTP Request

curl -X POST http://localhost:8000/ \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
      "name": "web_search",
      "arguments": {"query": "Python async", "all_results": true, "region": "us-en", "safesearch": "moderate", "timelimit": "w"}
    }
  }'

MCP Protocol

Initialize:

{"method": "initialize", "id": 1}

List Tools:

{"method": "tools/list", "id": 2}

Call Search:

{
  "method": "tools/call",
  "id": 3,
  "params": {
    "name": "web_search",
    "arguments": {"query": "your query", "max_results": 5, "all_results": false, "region": "wt-wt", "safesearch": "moderate"}
  }
}

LM Studio Configuration

Add to mcp-config.json:

{
  "mcpServers": {
    "duckduckgo-search": {
      "type": "http",
      "url": "http://localhost:8000",
      "disabled": false
    }
  }
}

Architecture

Data Flow:

LM Studio → POST / (JSON-RPC) → FastAPI → SSE Events → Client
                                    ↓
                            MCP Handlers (initialize, tools/list, tools/call)
                                    ↓
                            DuckDuckGo Search API

Key Components:

• mcp_http_sse_server.py - Single FastAPI app with async streaming

• requirements.txt - Dependencies only

• Dockerfile - Production & dev stages

• docker-compose.yml - Orchestration

• test-mcp-sse.py - Integration tests

Development

Make Commands

make help              # Show all commands
make install-dev      # Install dev dependencies
make run-dev          # Run with auto-reload
make test             # Run tests
make lint             # Ruff + mypy type checking
make format           # Black + ruff auto-format
make clean            # Remove build artifacts
make docker-build     # Build Docker image
make docker-compose   # Run with Docker Compose

Code Quality

make format  # Auto-format code
make lint    # Run linters

Configuration

Environment Variables

Docker

Set in docker-compose.yml:

environment:
  - MCP_ENVIRONMENT=development
  - MCP_LOG_LEVEL=INFO

Production requires: mcp_http_sse_server.py + requirements.txt

API Reference

Response Format (SSE)

event: message
data: {"jsonrpc": "2.0", "id": 1, "result": {...}}

event: done
data: {}

Search Tool

• Input:

  • query (string, required)

  • max_results (int, 1-10, default 5). Ignored when all_results is true.

  • all_results (boolean, default false): fetch maximum results (capped at 10)

  • region (string, default wt-wt)

  • safesearch (string enum: off|moderate|strict, default moderate)

  • timelimit (string enum: d|w|m|y, optional)

• Output: Formatted markdown with title, URL, snippet

Contributing

Keep this project minimal:

1. Single-file design (avoid modularization)

2. MCP protocol compliance

3. Test with real LM Studio

4. Run make format && make lint before submission

Support

• Examples: test-mcp-sse.py

• Debugging: Set MCP_DEBUG=true

• Config: See mcp-config.json

Changelog

• 1.2.0: Add all_results flag (internal cap 100) to support larger result sets; bump version and docs.

• 1.1.0: Improved DuckDuckGo search with region/safesearch/timelimit parameters; better markdown formatting; version surfaced in initialize.

License

See LICENSE