deep-research-mcp

# 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 1. Clone and install: ```bash git clone https://github.com/Ozamatash/deep-research cd deep-research npm install ``` 2. Set up environment in `.env.local`: ```bash # Copy the example environment file cp .env.example .env.local ``` 3. Build: ```bash # Build the server npm run build ``` 4. Run the cli version: ```bash npm run start ``` 5. 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 For remote servers: Streamable HTTP ```bash npm run start:http ``` Server runs on `http://localhost:3000/mcp` without session management. ## 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 ```mermaid flowchart TB subgraph Input Q[User Query] B[Breadth Parameter] D[Depth Parameter] FQ[Feedback Questions] end subgraph Research[Deep Research] direction TB SQ[Generate SERP Queries] SR[Search] RE[Source Reliability Evaluation] PR[Process Results] end subgraph Results[Research Output] direction TB L((Learnings with Reliability Scores)) SM((Source Metadata)) ND((Next Directions: Prior Goals, New Questions)) end %% Main Flow Q & FQ --> CQ[Combined Query] CQ & B & D --> SQ SQ --> SR SR --> RE RE --> PR %% Results Flow PR --> L PR --> SM PR --> ND %% Depth Decision and Recursion L & ND --> DP{depth > 0?} DP -->|Yes| SQ %% Final Output DP -->|No| MR[Markdown Report] %% Styling classDef input fill:#7bed9f,stroke:#2ed573,color:black classDef process fill:#70a1ff,stroke:#1e90ff,color:black classDef output fill:#ff4757,stroke:#ff6b81,color:black classDef results fill:#a8e6cf,stroke:#3b7a57,color:black,width:150px,height:150px class Q,B,D,FQ input class SQ,SR,RE,PR process class MR output class L,SM,ND results ``` ## 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: 1. Set up local Firecrawl: ```bash git clone https://github.com/Ozamatash/localfirecrawl cd localfirecrawl # Follow setup in localfirecrawl README ``` 2. Update `.env.local`: ```bash FIRECRAWL_BASE_URL="http://localhost:3002" ``` ### Optional: Observability Add observability to track research flows, queries, and results using Langfuse: ```bash # Add to .env.local LANGFUSE_PUBLIC_KEY="your_langfuse_public_key" LANGFUSE_SECRET_KEY="your_langfuse_secret_key" ``` The app works normally without observability if no Langfuse keys are provided. ## License MIT License