-
securityA
license-
qualityEnables iterative deep research by integrating AI agents with search engines, web scraping, and large language models for efficient data gathering and comprehensive reporting.
Last updated -
4
276
MIT License
The DeepResearch MCP server enables intelligent, automated research processing through the Model Context Protocol (MCP). With this server, you can:
Initialize research sessions: Start new research with a specified query and optional depth parameter
Conduct iterative research: Execute research steps sequentially using session IDs
Generate comprehensive reports: Create structured final reports from completed sessions
Perform complete research: Conduct end-to-end research in a single command from query to final report
Manage research sessions: Track progress using session IDs and access research state
Process complex topics: Handle large research topics with configurable depth and timeout parameters
Utilize advanced tools: Leverage web search (via Firecrawl) and AI analysis (via OpenAI) to gather and process information
Uses environment variables for configuration of API keys and other settings, with support for .env files
Runs on Node.js as the underlying runtime environment, requiring Node.js 18 or higher for operation
Leverages OpenAI for analysis and report generation as part of the research workflow, processing collected information into structured knowledge
Built using TypeScript for type-safe implementation of the research assistant functionality
DeepResearch MCP
📚 Overview
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.
🌟 Key Features
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
🛠️ Architecture
┌────────────────────┐ ┌─────────────────┐ ┌────────────────┐
│ │ │ │ │ │
│ MCP Server Layer ├────►│ Research Service├────►│ Search Service │
│ (Tools & Prompts) │ │ (Session Mgmt) │ │ (Firecrawl) │
│ │ │ │ │ │
└────────────────────┘ └─────────┬───────┘ └────────────────┘
│
▼
┌─────────────────┐
│ │
│ OpenAI Service │
│ (Analysis/Rpt) │
│ │
└─────────────────┘
💻 Installation
Prerequisites
Node.js 18 or higher
OpenAI API key
Firecrawl API key
Setup Steps
Clone the repository
git clone <repository-url> cd deep-research-mcpInstall dependencies
npm installConfigure environment variables
cp .env.example .envEdit the
.envfile and add your API keys:OPENAI_API_KEY=sk-your-openai-api-key FIRECRAWL_API_KEY=your-firecrawl-api-keyBuild the project
npm run build
🚀 Usage
Running the MCP Server
Start the server on stdio for MCP client connections:
npm start
Using the Example Client
Run research on a specific topic with a specified depth:
npm run client "Your research topic" 3
Parameters:
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)
Example:
npm run client "the impact of climate change on coral reefs" 3 complete
Example Output
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
🔧 MCP Integration
Available MCP Resources
Resource Path | Description |
| Access the current state of a research session |
| Access the collected findings for a session |
Available MCP Tools
Tool Name | Description | Parameters |
| Start a new research session |
: string,
: number |
| Execute the next research step |
: string |
| Create a final report |
: string,
: number (optional) |
| Execute the entire research process |
: string,
: number,
: number (optional) |
🖥️ Claude Desktop Integration
DeepResearch MCP can be integrated with Claude Desktop to provide direct research capabilities to Claude.
Configuration Steps
Copy the sample configuration
cp claude_desktop_config_sample.json ~/path/to/claude/desktop/config/directory/claude_desktop_config.jsonEdit the configuration file
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" } } } }Restart Claude Desktop
After saving the configuration, restart Claude Desktop for the changes to take effect.
Using with Claude Desktop
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?
📋 Sample Client Code
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);
🔍 Troubleshooting
Common Issues
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 the
complete-researchtool with a longer timeoutProcess 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
📝 License
ISC
🙏 Acknowledgements
Built with Model Context Protocol
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
A powerful research assistant that conducts intelligent, iterative research through web searches, analysis, and comprehensive report generation on any topic.
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