Open Deep Research MCP Server
An AI-powered research assistant that performs deep, iterative research on any topic. It combines search engines, web scraping, and AI to explore topics in depth and generate comprehensive reports. Available as a Model Context Protocol (MCP) tool or standalone CLI. Look at exampleout.md to see what a report might look like.
Quick Start
- Clone and install:
- Set up environment in
.env.local:
- Build:
- Run the cli version:
- Test MCP Server with Claude Desktop:Follow the guide thats at the bottom of server quickstart to add the server to Claude Desktop:https://modelcontextprotocol.io/quickstart/server
Features
- Performs deep, iterative research by generating targeted search queries
- Controls research scope with depth (how deep) and breadth (how wide) parameters
- Evaluates source reliability with detailed scoring (0-1) and reasoning
- Prioritizes high-reliability sources ( 0.7) and verifies less reliable information
- Generates follow-up questions to better understand research needs
- Produces detailed markdown reports with findings, sources, and reliability assessments
- Available as a Model Context Protocol (MCP) tool for AI agents
- For now MCP version doesn't ask follow up questions
How It Works
Advanced Setup
Using Local Firecrawl (Free Option)
Instead of using the Firecrawl API, you can run a local instance. You can use the official repo or my fork which uses searXNG as the search backend to avoid using a searchapi key:
- Set up local Firecrawl:
- Update
.env.local:
Optional: Observability
Add observability to track research flows, queries, and results using Langfuse:
The app works normally without observability if no Langfuse keys are provided.
License
MIT License
Open Deep Research MCP Server
An AI-powered research assistant that performs deep, iterative research on any topic. It combines search engines, web scraping, and AI to explore topics in depth and generate comprehensive reports. Available as a Model Context Protocol (MCP) tool or standalone CLI. Look at exampleout.md to see what a report might look like.
Quick Start
- Clone and install:
- Set up environment in
.env.local:
- Build:
- Run the cli version:
- Test MCP Server with Claude Desktop:Follow the guide thats at the bottom of server quickstart to add the server to Claude Desktop:https://modelcontextprotocol.io/quickstart/server
Features
- Performs deep, iterative research by generating targeted search queries
- Controls research scope with depth (how deep) and breadth (how wide) parameters
- Evaluates source reliability with detailed scoring (0-1) and reasoning
- Prioritizes high-reliability sources ( 0.7) and verifies less reliable information
- Generates follow-up questions to better understand research needs
- Produces detailed markdown reports with findings, sources, and reliability assessments
- Available as a Model Context Protocol (MCP) tool for AI agents
- For now MCP version doesn't ask follow up questions
How It Works
Advanced Setup
Using Local Firecrawl (Free Option)
Instead of using the Firecrawl API, you can run a local instance. You can use the official repo or my fork which uses searXNG as the search backend to avoid using a searchapi key:
- Set up local Firecrawl:
- Update
.env.local:
Optional: Observability
Add observability to track research flows, queries, and results using Langfuse:
The app works normally without observability if no Langfuse keys are provided.
License
MIT License