DeepResearch MCP

A powerful research assistant for conducting iterative web searches, analysis, and report generation.

DeepResearch MCP is a powerful research assistant built on the Model Context Protocol (MCP). It conducts intelligent, iterative research on any topic through web searches, analysis, and comprehensive report generation.

- Intelligent Topic Exploration- Automatically identifies knowledge gaps and generates focused search queries
- Comprehensive Content Extraction- Enhanced web scraping with improved content organization
- Structured Knowledge Processing- Preserves important information while managing token usage
- Scholarly Report Generation- Creates detailed, well-structured reports with executive summaries, analyses, and visualizations
- Complete Bibliography- Properly cites all sources with numbered references
- Adaptive Content Management- Automatically manages content to stay within token limits
- Error Resilience- Recovers from errors and generates partial reports when full processing isn't possible

┌────────────────────┐ ┌─────────────────┐ ┌────────────────┐ │ │ │ │ │ │ │ MCP Server Layer ├────►│ Research Service├────►│ Search Service │ │ (Tools & Prompts) │ │ (Session Mgmt) │ │ (Firecrawl) │ │ │ │ │ │ │ └────────────────────┘ └─────────┬───────┘ └────────────────┘ │ ▼ ┌─────────────────┐ │ │ │ OpenAI Service │ │ (Analysis/Rpt) │ │ │ └─────────────────┘

- Node.js 18 or higher
- OpenAI API key
- Firecrawl API key

git clone <repository-url> cd deep-research-mcp

Edit the.envfile and add your API keys:

OPENAI_API_KEY=sk-your-openai-api-key FIRECRAWL_API_KEY=your-firecrawl-api-key

Start the server on stdio for MCP client connections:

Run research on a specific topic with a specified depth:

npm run client "Your research topic" 3

- First argument: Research topic or query
- Second argument: Research depth (number of iterations, default: 2)
- Third argument (optional): "complete" to use the complete-research tool (one-step process)

npm run client "the impact of climate change on coral reefs" 3 complete

The DeepResearch MCP will produce a comprehensive report that includes:

- Executive Summary- Concise overview of the research findings
- Introduction- Context and importance of the research topic
- Methodology- Description of the research approach
- Comprehensive Analysis- Detailed examination of the topic
- Comparative Analysis- Visual comparison of key aspects
- Discussion- Interpretation of findings and implications
- Limitations- Constraints and gaps in the research
- Conclusion- Final insights and recommendations
- Bibliography- Complete list of sources with URLs

DeepResearch MCP can be integrated with Claude Desktop to provide direct research capabilities to Claude.

cp claude_desktop_config_sample.json ~/path/to/claude/desktop/config/directory/claude_desktop_config.json

Update the path to point to your installation of deep-research-mcp and add your API keys:

{ "mcpServers": { "deep-research": { "command": "node", "args": [ "/absolute/path/to/your/deep-research-mcp/dist/index.js" ], "env": { "FIRECRAWL_API_KEY": "your-firecrawler-api-key", "OPENAI_API_KEY": "your-openai-api-key" } } } }

After saving the configuration, restart Claude Desktop for the changes to take effect.

Now you can ask Claude to perform research using commands like:

Can you research the impact of climate change on coral reefs and provide a detailed report?

import { Client } from "@modelcontextprotocol/sdk/client/index.js"; import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js"; async function main() { // Connect to the server const transport = new StdioClientTransport({ command: "node", args: ["dist/index.js"] }); const client = new Client({ name: "deep-research-client", version: "1.0.0" }); await client.connect(transport); // Initialize research const initResult = await client.callTool({ name: "initialize-research", arguments: { query: "The impact of artificial intelligence on healthcare", depth: 3 } }); // Parse the response to get sessionId const { sessionId } = JSON.parse(initResult.content[0].text); // Execute steps until complete let currentDepth = 0; while (currentDepth < 3) { const stepResult = await client.callTool({ name: "execute-research-step", arguments: { sessionId } }); const stepInfo = JSON.parse(stepResult.content[0].text); currentDepth = stepInfo.currentDepth; console.log(Completed step ${stepInfo.currentDepth}/${stepInfo.maxDepth}); } // Generate final report with timeout const report = await client.callTool({ name: "generate-report", arguments: { sessionId, timeout: 180000 // 3 minutes timeout } }); console.log("Final Report:"); console.log(report.content[0].text); } main().catch(console.error);

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Token Limit Exceeded: For very large research topics, you may encounter OpenAI token limit errors. Try:

- Reducing the research depth
- Using more specific queries
- Breaking complex topics into smaller sub-topics

Timeout Errors: For complex research, the process may time out. Solutions:

- Increase the timeout parameters in tool calls
- Use thecomplete-researchtool with a longer timeout
- Process research in smaller chunks

API Rate Limits: If you encounter rate limit errors from OpenAI or Firecrawl:

- Implement a delay between research steps
- Use an API key with higher rate limits
- Retry with exponential backoff

- Built withModel Context Protocol
- Powered byOpenAIandFirecrawl

Real-time web data, structured for agents