Octagon Deep Research MCP

The Octagon Deep Research MCP server provides specialized AI-powered research capabilities designed for developers and technical teams. From complex debugging and performance optimization to market research and framework comparison, it delivers comprehensive analysis that goes straight from research to working applications. No rate limits, faster than ChatGPT Deep Research, more thorough than Grok DeepSearch or Perplexity Deep Research. Add unlimited deep research functionality to any MCP client including Claude Desktop, Cursor, and other popular MCP-enabled applications.

Connecting to the Octagon Deep Research MCP Server

The Octagon Deep Research MCP server is available at:

• https://mcp.octagonagents.com/deep-research/mcp for Remote MCP clients that support OAuth (recommended)

Using with Cursor

Option 1: One-Click Install (Recommended)

Option 2: Manual Configuration

1. Go to your Cursor editor's settings
2. Navigate to "Tools & Integrations" > "New MCP Server"
3. Add the following to your mcp.json

{
  "mcpServers": {
    "octagonDeepResearch": {
      "url": "https://mcp.octagonagents.com/deep-research/mcp"
    }
  }
}

4. Save your mcp.json file and authenticate with OAuth when prompted

Using with Claude.ai

Note: Requires Claude Pro, Team, or Enterprise

For Claude.ai Admins

Only Workspace Owners and Primary Owners can set up MCP server connections in Claude.ai:

1. Go to Settings in Claude.ai
2. Navigate to the "Integrations" section
3. Click "Add integration"
4. For Integration name, enter "Octagon Deep Research"
5. For Integration URL, enter https://mcp.octagonagents.com/deep-research/mcp
6. Click "Add"
7. Select which Octagon Deep Research tools to enable for your workspace
8. Click "Save"

For Claude.ai Users

After your admin has set up the integration:

Navigate to claude.ai Click on the tools menu (next to the search icon) Select "Octagon Deep Research" from the list of available integrations If this is your first time using the integration, you'll be prompted to authenticate Once authenticated, you can start using Claude with Octagon Deep Research

Running on Claude Desktop

To configure Octagon Deep Research MCP for Claude Desktop:

1. Open Claude Desktop
2. Go to Settings > Developer > Edit Config
3. Add the following to your claude_desktop_config.json (Replace YOUR_OCTAGON_API_KEY_HERE with your Octagon API key):

{
  "mcpServers": {
    "octagonDeepResearch": {
      "command": "npx",
      "args": ["-y", "octagon-deep-research-mcp@latest"],
      "env": {
        "OCTAGON_API_KEY": "YOUR_OCTAGON_API_KEY_HERE"
      }
    }
  }
}

4. Restart Claude for the changes to take effect

Get Your Octagon API Key

To use Octagon Deep Research MCP, you need to:

1. Sign up for a free account at Octagon
2. After logging in, from left menu, navigate to API Keys
3. Generate a new API key
4. Use this API key in your configuration as the OCTAGON_API_KEY value

Features & Example Prompts

✅ Complex Debugging

• Root cause analysis across distributed systems
• Performance bottleneck identification
• Memory leak detection and analysis
• Cross-platform compatibility issue resolution
• Database query optimization debugging
• Network latency and connectivity troubleshooting

Example Prompt:

Research the latest techniques for spotting memory leaks in large React + Node.js projects. Then use what you learn to build a command-line analyzer that scans a codebase, reports suspect leaks, and suggests the best fix for each.

✅ Market Research

• Competitive landscape analysis and positioning
• Industry trend identification and forecasting
• Customer behavior and preference analysis
• Market size and growth opportunity assessment
• Pricing strategy research and recommendations
• Regulatory and compliance landscape mapping

Example Prompt:

Study AI graphic-design apps made for non-designers—competitors, gaps, user pains, pricing, and trends. Turn the insights into a live web app prototype that lets a user drop in text prompts and instantly receive design suggestions based on top-requested features.

✅ Finding Optimal Packages

• Dependency compatibility analysis
• Performance benchmarking of alternatives
• Security vulnerability assessment
• Maintenance and support evaluation
• License compatibility verification
• Bundle size impact analysis

Example Prompt:

Compare Python libraries for real-time chat (FastAPI + WebSockets, Django Channels, Socket.IO, etc.). Build a runnable chat server + client demo with the best stack, including Docker setup and sample messaging UI.

✅ API Research

• REST and GraphQL API documentation analysis
• Rate limiting and authentication method comparison
• SDK availability and quality assessment
• API reliability and uptime evaluation
• Integration complexity and development effort estimation
• Cost structure and pricing model analysis

Example Prompt:

Evaluate leading video-streaming APIs on cost, latency, uptime, and docs quality. Use the top pick to create a working mini-site that can sign in, start a live stream, and play archived videos.

✅ Framework Comparison

• Performance benchmarking and scalability analysis
• Learning curve and developer experience evaluation
• Community support and ecosystem maturity assessment
• Long-term maintenance and update frequency analysis
• Integration capabilities and plugin availability
• Production deployment and hosting requirements

Example Prompt:

Bench-test Next.js, Remix, and Astro for high-traffic e-commerce (build speed, runtime performance, SEO). With the winner, spin up a functional storefront: product page, cart flow, SSR, and basic tests.

✅ Tool Discovery

• Development workflow optimization recommendations
• CI/CD pipeline tool evaluation and selection
• Testing framework and tool comparison
• Code quality and security analysis tool research
• Project management and collaboration platform assessment
• Monitoring and observability solution discovery

Example Prompt:

Survey modern end-to-end testing tools for web + mobile (Playwright, Maestro, etc.). Set up the best option in a sandbox project and expose a simple dashboard that shows real-time test results.

✅ Performance Optimizations

• Code profiling and hotspot identification
• Database query optimization strategies
• Caching layer implementation recommendations
• CDN and asset delivery optimization
• Memory usage and garbage collection tuning
• Network request batching and optimization

Example Prompt:

Collect proven tricks for speeding up PostgreSQL on AWS, Azure, and GCP—index patterns, query tuning, caching, and monitoring. Package them into a small 'auto-tune' service that applies recommended settings and shows before/after metrics.

✅ Design Overhauls

• UI/UX pattern analysis and best practices
• Accessibility compliance and implementation guidance
• Component library and design system evaluation
• Responsive design and mobile optimization strategies
• Color theory and brand consistency recommendations
• User journey mapping and conversion optimization

Example Prompt:

Research current UX/UI patterns that boost engagement in enterprise SaaS dashboards. Build an interactive dashboard template—navigation, widgets, theme switcher—showcasing the most impactful patterns for immediate reuse.

🏆 Why Development Teams Choose Octagon's Enterprise-Grade Deep Research API

👉 8–10x faster than the leading incumbent—debug complex issues, compare frameworks, and research APIs in seconds, not minutes

👉 Greater depth & accuracy —pulls data from 3x more high-quality sources and cross-checks every technical detail and performance metric

👉 Unlimited parallel runs—no rate caps, so your developers can launch as many research tasks as they need for package evaluation, tool discovery, and performance optimization (unlike ChatGPT Pro's 125-task monthly limit)

🚀 Core Differentiators

✅ No Rate Limits - Execute unlimited debugging sessions, package comparisons, and API research without restrictions (vs ChatGPT Pro's 125-task monthly limit)

✅ Superior Performance - Faster than ChatGPT Deep Research, more thorough than Grok DeepSearch or Perplexity Deep Research

✅ Enterprise-Grade Speed - 8-10x faster than leading incumbents, with 3x more source coverage for technical research

✅ Universal MCP Integration - Add deep research functionality to any MCP client including Cursor, Claude Desktop, and other dev tools

✅ Developer-Focused Expertise - Specialized research across debugging, performance optimization, framework comparison, and technical decision-making

✅ Advanced Data Synthesis - Multi-source aggregation with cross-verification of every technical detail and performance metric

What is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is an open standard that allows AI applications to extend models with additional capabilities and context. Think of MCP as a "USB-C port for AI applications" - it provides a standardized way for AI models to connect with external tools, data sources, and specialized capabilities.

MCP enables AI models to:

• Access external knowledge and data sources
• Use specialized tools for specific domains
• Leverage capabilities beyond what's built into the base model

The Octagon Deep Research MCP server implements this protocol to provide specialized comprehensive research capabilities directly to your preferred AI tools and interfaces like Cursor, Claude Desktop, and other MCP-enabled applications.

For a deeper understanding of the MCP protocol, see Anthropic's MCP documentation.

Resources

• Octagon Deep Research Documentation - Learn more about the Deep Research Agent
• Octagon MCP Server GitHub Repository - Source code and additional documentation
• Anthropic MCP Documentation - More information about the MCP protocol