DuckDuckGo Browser MCP Server
Enables real-time internet searches via DuckDuckGo Lite with automatic retry, smart caching, and category detection for reliable results.
README
DuckDuckGo Browser MCP Server
A production-ready Model Context Protocol (MCP) server that performs real-time internet searches via the DuckDuckGo Lite endpoint (https://lite.duckduckgo.com/lite/) with automatic retry logic, intelligent caching, and robust error handling across all transport modes.
Previously: Simulated search with a deterministic knowledge base
Now: Live web search results with enterprise-grade reliability
What's New
⨠Real-Time Search: Fetch live results from DuckDuckGo Lite
š Automatic Retries: 3 attempts with exponential backoff
š¾ Smart Caching: 5-minute TTL with stale-data fallback
ā±ļø Timeout Protection: Request-level and session-level timeouts
š Auto-Detection: Category detection from search results
š”ļø Robust Modes: Enhanced error handling for STDIO, SSE, and Streamable HTTP
š Detailed Logging: Debug-friendly operation tracking
Folder Structure
duckduckgo-browser-mcp/
āāā src/
ā āāā duckduckgo_browser/
ā āāā __init__.py # Package entry point (sync main())
ā āāā __main__.py # python -m duckduckgo_browser
ā āāā server.py # MCP app + all three transport modes
ā āāā services/
ā ā āāā __init__.py
ā ā āāā search_engine.py # Real-time search via DuckDuckGo Lite
ā ā āāā web_scraper.py # Web scraper with retry logic & caching
ā āāā tools/
ā āāā __init__.py
ā āāā toolhandler.py # Abstract base class for tools
ā āāā search_tools.py # get_internet_result tool handler
āāā tests/
ā āāā __init__.py
ā āāā test_search_tools.py
āāā Dockerfile
āāā pyproject.toml
āāā pytest.ini
āāā README.md
Installation
cd duckduckgo-browser-mcp
python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate
pip install -e .
Dependencies:
- mcp[cli]>=1.12.0
- starlette>=0.27.0
- uvicorn>=0.20.0
- requests>=2.31.0 (HTTP client)
- beautifulsoup4>=4.12.0 (HTML parsing)
Running the Server
1. STDIO mode
Used by MCP hosts like Claude Desktop, IDE plugins, and CLI clients.
python -m duckduckgo_browser --mode stdio
The server reads JSON-RPC frames from stdin and writes responses to stdout.
2. SSE mode (Server-Sent Events)
python -m duckduckgo_browser --mode sse --host 0.0.0.0 --port 7070
GET http://localhost:7070/sseā open SSE streamPOST http://localhost:7070/messages/ā send MCP request frames
3. Streamable HTTP mode (Recommended for Production)
python -m duckduckgo_browser --mode streamable-http --host 0.0.0.0 --port 7070
POST http://localhost:7070/mcpā single endpoint, chunked streaming response
Docker
# Build
docker build -t duckduckgo-browser-mcp .
# Run (streamable-http, default)
docker run -p 7070:7070 duckduckgo-browser-mcp
# Run SSE mode
docker run -p 7070:7070 duckduckgo-browser-mcp --mode sse --port 7070
# Run STDIO mode (pipe-based)
docker run -i duckduckgo-browser-mcp --mode stdio
Tool: get_internet_result
Performs real-time searches on DuckDuckGo Lite with automatic retry and caching.
| Field | Type | Required | Description |
|---|---|---|---|
input_value |
string | ā | Natural language query or search term |
Example output
# DuckDuckGo Browser -- Search Results
- **Query:** "what is machine learning"
- **Topic category detected:** ai
- **Results found:** 5
## Top Results
### 1. Machine Learning Explained
- **URL:** https://www.ibm.com/topics/machine-learning
- **Summary:** Machine learning is a subset of AI ...
- **Relevance score:** 5
## Suggested Websites to Explore
### Hugging Face
- **URL:** https://huggingface.co
- **Why visit:** Leading hub for open-source AI models and datasets
## What to Look For
When researching AI topics, prioritise peer-reviewed papers (arXiv, NeurIPS)
and official model documentation. Cross-check benchmark claims on Papers With Code.
---
*Results powered by DuckDuckGo Lite (https://lite.duckduckgo.com/lite/).*
*Search performed in real-time with automatic retry and caching for reliability.*
Example MCP Client Calls
Python (stdio)
import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
async def main():
params = StdioServerParameters(
command="python",
args=["-m", "duckduckgo_browser", "--mode", "stdio"],
)
async with stdio_client(params) as (read, write):
async with ClientSession(read, write) as session:
await session.initialize()
result = await session.call_tool(
"get_internet_result",
{"input_value": "what is machine learning"},
)
print(result.content[0].text)
asyncio.run(main())
Python (SSE)
import asyncio
from mcp import ClientSession
from mcp.client.sse import sse_client
async def main():
async with sse_client("http://localhost:7070/sse") as (read, write):
async with ClientSession(read, write) as session:
await session.initialize()
result = await session.call_tool(
"get_internet_result",
{"input_value": "best travel destinations Europe"},
)
print(result.content[0].text)
asyncio.run(main())
Python (Streamable HTTP)
import asyncio
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
async def main():
async with streamablehttp_client("http://localhost:7070/mcp") as (read, write, _):
async with ClientSession(read, write) as session:
await session.initialize()
result = await session.call_tool(
"get_internet_result",
{"input_value": "how does quantum computing work"},
)
print(result.content[0].text)
asyncio.run(main())
curl (Streamable HTTP ā initialize)
curl -X POST http://localhost:7070/mcp \
-H "Content-Type: application/json" \
-d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"curl-client","version":"1.0"}}}'
curl (Streamable HTTP ā call tool)
# Replace <SESSION_ID> with the mcp-session-id from the initialize response header
curl -X POST http://localhost:7070/mcp \
-H "Content-Type: application/json" \
-H "mcp-session-id: <SESSION_ID>" \
-d '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"get_internet_result","arguments":{"input_value":"python web frameworks"}}}'
Running Tests
pip install pytest pytest-asyncio
pytest
How It Works
Search Flow
User Query
ā
get_internet_result tool (async)
ā
RealSearchEngine.search()
ā
DuckDuckGoScraper.search_with_retry()
āā Check in-memory cache
āā If cached & valid ā return cached results
āā If cache miss ā fetch from DuckDuckGo Lite
ā āā Attempt 1: Request + parse (10s timeout)
ā āā Timeout? ā wait 2s, retry
ā āā Attempt 2: Request + parse
ā āā Timeout? ā wait 4s, retry
ā āā Attempt 3: Request + parse
ā āā All fail? ā return stale cache or empty
āā Cache new results (5 min TTL)
ā
Auto-detect Category
ā
Curated Website Suggestions
ā
Formatted Markdown Response
Key Features
Retry Logic
- Maximum 3 attempts per query
- Exponential backoff: 2s, 4s between retries
- Timeout per request: 10 seconds
Caching
- In-memory cache with 5-minute TTL
- Prevents repeated web calls for same query
- Stale cache returned on network failures
- Automatic eviction of expired entries
Error Handling
- Timeout errors trigger retry logic
- Connection errors check cache
- Network failures return cached data or graceful error message
- STDIO: Catches BrokenPipeError on client disconnect
- SSE: Per-session timeout (1 hour)
- Streamable HTTP: Per-request timeout (60 seconds)
Category Detection Results are automatically categorized based on content patterns:
- AI, programming, cloud, finance, health, science, education, travel, food, sports, general
Each category includes curated website suggestions.
Configuration
Adjust scraper behavior in src/duckduckgo_browser/services/web_scraper.py:
DuckDuckGoScraper(
timeout=10.0, # Request timeout (seconds)
max_retries=3, # Retry attempts
retry_backoff_base=2.0, # Exponential backoff base
cache_ttl=300, # Cache TTL (seconds)
)
Adjust HTTP server timeouts in src/duckduckgo_browser/server.py:
- STDIO: Standard input timeout
- SSE: 3600s session timeout
- Streamable HTTP: 60s request timeout
Logging
Server provides detailed logging at multiple levels:
Startup Output
2026-04-01T15:36:13 INFO duckduckgo-browser ================================================================================
2026-04-01T15:36:13 INFO duckduckgo-browser Starting DuckDuckGo Browser MCP Server
2026-04-01T15:36:13 INFO duckduckgo-browser Python: 3.11.6
2026-04-01T15:36:13 INFO duckduckgo-browser Mode: streamable-http
2026-04-01T15:36:13 INFO duckduckgo-browser Listening at http://0.0.0.0:7070
2026-04-01T15:36:13 INFO duckduckgo-browser Registered tools: ['get_internet_result']
Log Levels
- DEBUG: Cache hits/misses, individual retry attempts, parser details
- INFO: Tool registration, server startup, search requests
- WARNING: Retry attempts, network failures, cache misses
- ERROR: Fatal errors, unrecoverable failures
Enable debug logging:
import logging
logging.basicConfig(level=logging.DEBUG)
Robustness Summary
| Feature | Details |
|---|---|
| Network Retry | 3x with exponential backoff (2s, 4s) |
| Request Timeout | 10 seconds per attempt |
| Session Timeout | 60s HTTP, 1h SSE, unlimited STDIO |
| Caching | 5-min TTL + stale fallback |
| Error Recovery | Graceful degradation, no crashes |
| STDIO Mode | Handles BrokenPipeError, client disconnect |
| SSE Mode | Connection error handling, CORS support |
| Streamable HTTP | Error responses (504 timeout, 500 error) |
| Logging | DEBUG/INFO/WARNING/ERROR levels |
Known Limitations
-
DuckDuckGo Lite Parse: Assumes standard DuckDuckGo Lite HTML format
- May need updates if DDG changes HTML structure
- Parser handles missing/malformed results gracefully
-
Network Dependency: Requires internet connectivity
- Uses cache to survive brief outages
- Falls back to empty results after 3 retries
-
Rate Limiting: DuckDuckGo may rate-limit rapid requests
- Exponential backoff helps mitigate
- Cache reduces repeated queries
-
In-Memory Cache: Lost on server restart
- Suitable for most deployments
- Consider persistent cache (Redis/SQLite) for high-volume use
Troubleshooting
Port Already in Use
python -m duckduckgo_browser --mode streamable-http --port 8000
Connection Timeout
- Verify internet connectivity
- Test:
curl https://lite.duckduckgo.com/lite/?q=test - Check firewall rules
No Results
- Verify query is valid
- Check logs: Enable DEBUG level logging
- Test with simpler query terms
Dependency Issues
pip install -r requirements.txt
# or
pip install -e .
Files Modified
| File | Change | Purpose |
|---|---|---|
services/web_scraper.py |
NEW | DuckDuckGo Lite scraper with retry/cache |
services/search_engine.py |
Modified | Live search engine (was deterministic) |
services/__init__.py |
Modified | Export new functions |
tools/search_tools.py |
Modified | Async tool handler |
server.py |
Modified | Enhanced error handling & timeouts |
pyproject.toml |
Modified | Added requests + beautifulsoup4 |
Future Enhancements
- [ ] Persistent cache (Redis/SQLite)
- [ ] Circuit breaker pattern for API failures
- [ ] Result ranking/scoring improvements
- [ ] Support for other search engines
- [ ] Metrics/monitoring integration
- [ ] Request deduplication
- [ ] Result filtering/quality scoring
Version History
v1.0.0 (April 2026) - Live DuckDuckGo Search
- Real-time search via DuckDuckGo Lite endpoint
- Automatic retry with exponential backoff
- Smart result caching (5-min TTL)
- Enhanced error handling across all modes
- Auto-category detection
- Comprehensive logging
v0.1.0 (Previous) - Deterministic Knowledge Base
- Static internal knowledge base
- No network dependencies
- Deterministic results
Status: Production Ready ā
Last Updated: April 1, 2026
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.