What can you do with this server?

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

Which integrations are available for this server?

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

How do I use DeepResearch MCP?

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., "@DeepResearch MCP research the latest advancements in quantum computing" 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.

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DeepResearch MCP

by ameeralns

Overview Schema Related Servers Score Discussions

TypeScript

Remote

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

Related MCP server: OpenDeepSearch

🛠️ 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-mcp

Install dependencies
```
npm install
```

Configure environment variables

cp .env.example .env

Edit the .env file and add your API keys:

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

Build 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
`research://state/{sessionId}`	Access the current state of a research session
`research://findings/{sessionId}`	Access the collected findings for a session

Available MCP Tools

Tool Name	Description	Parameters
`initialize-research`	Start a new research session	`query`: string, `depth`: number
`execute-research-step`	Execute the next research step	`sessionId`: string
`generate-report`	Create a final report	`sessionId`: string, `timeout`: number (optional)
`complete-research`	Execute the entire research process	`query`: string, `depth`: number, `timeout`: 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.json

Edit 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-research tool 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

📝 License

ISC

🙏 Acknowledgements

Built with Model Context Protocol
Powered by OpenAI and Firecrawl

Available Tools

4 tools

complete-researchD

ParametersJSON Schema

Name	Required	Description	Default
`depth`	No
`query`	Yes
`timeout`	No

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

execute-research-stepD

ParametersJSON Schema

Name	Required	Description	Default
`sessionId`	Yes

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generate-reportD

ParametersJSON Schema

Name	Required	Description	Default
`sessionId`	Yes
`timeout`	No

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

initialize-researchD

ParametersJSON Schema

Name	Required	Description	Default
`depth`	No
`query`	Yes

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

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TDQS

D1.6/5.0

Disambiguation2/5

The tools have overlapping purposes with unclear boundaries, as 'complete-research', 'execute-research-step', and 'initialize-research' all seem to relate to conducting research without clear distinctions. The lack of descriptions exacerbates ambiguity, making it difficult to differentiate their specific roles in a research workflow.

Naming Consistency5/5

All tool names follow a consistent verb-noun pattern with hyphens (e.g., 'complete-research', 'execute-research-step'), showing a predictable and readable naming convention throughout the set. There are no deviations or mixed styles, making the naming highly consistent.

Tool Count3/5

With 4 tools, the count is borderline for a research domain, as it feels thin for covering a complete research lifecycle (e.g., data analysis, validation, collaboration). While it might handle basic steps, the limited number suggests potential gaps in functionality for a comprehensive research process.

Completeness2/5

The tool set shows significant gaps for a research domain, as it lacks operations for data gathering, analysis, validation, or collaboration (e.g., no tools for searching sources, analyzing data, or peer review). The surface is incomplete, likely causing agent failures in conducting thorough research without workarounds.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

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