Which integrations are available for this server?

The README references using Git to clone repositories for setup The README references GitHub as the source for cloning the repository The server produces detailed markdown reports with findings, sources, and reliability assessments The README contains a Mermaid diagram to explain the workflow of the deep research process The README mentions using a fork of Firecrawl that uses SearXNG as the search backend to avoid using a searchapi key

How do I use Deep Research MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Deep Research MCP Server research the latest advancements in quantum computing with depth 3 and breadth 2" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

DISCLAIMER

This repo is an experiment on agent coding. 95% of the code is written by LLM's

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:

git clone https://github.com/Ozamatash/deep-research
cd deep-research
npm install

Set up environment in .env.local:

# Copy the example environment file
cp .env.example .env.local

Build:

# Build the server
npm run build

Run the cli version:

npm run start

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

npm run start:http

Server runs on http://localhost:3000/mcp without session management.

Related MCP server: Octagon Deep Research MCP

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
Natural-language source preferences (avoid listicles, forums, affiliate reviews, specific domains)

Model Selection (OpenAI, Anthropic, Google, xAI)

Pick a provider and model per run.

CLI: you will be prompted for provider and model. Example: openai + gpt-5.2.
MCP/HTTP: pass model, e.g. openai:gpt-5.2 (also accepts openai/gpt-5.2).

Set the corresponding API key in .env.local:

OPENAI_API_KEY=...
ANTHROPIC_API_KEY=...
GOOGLE_API_KEY=...
XAI_API_KEY=...

Optionally set default models per provider:

OPENAI_MODEL=gpt-5.2
ANTHROPIC_MODEL=claude-opus-4-5
GOOGLE_MODEL=gemini-3-pro-preview
XAI_MODEL=grok-4-1-fast-reasoning

If you use a non-default OpenAI endpoint:

OPENAI_ENDPOINT=https://api.openai.com/v1

How It Works

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:

Set up local Firecrawl:

git clone https://github.com/Ozamatash/localfirecrawl
cd localfirecrawl
# Follow setup in localfirecrawl README

Update .env.local:

FIRECRAWL_BASE_URL="http://localhost:3002"

Optional: Observability

Add observability to track research flows, queries, and results using Langfuse:

# 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

Deep Research MCP Server