Content
# 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 "Your research query here"
```
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
## 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
