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ConsultingAgents MCP Server: Multi-API Collaboration & Smart Solutions - MCP Implementation

ConsultingAgents MCP Server: Multi-API Collaboration & Smart Solutions

ConsultingAgents MCP Server connects OpenAI & Anthropic APIs, giving Claude Code 'coworkers' to solve your toughest challenges. Smarter solutions, faster." )

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Ranked in the top 1% of all AI tools in its category

About ConsultingAgents MCP Server

What is ConsultingAgents MCP Server: Multi-API Collaboration & Smart Solutions?

ConsultingAgents MCP Server is a middleware framework enabling seamless integration between Claude Code and specialized AI agents (Darren, Sonny, Sergey) to resolve complex coding challenges. By leveraging multi-model collaboration, it unifies the strengths of distinct AI capabilities—such as Darren's advanced reasoning, Sonny's deep analysis, and Sergey's contextual search—to deliver optimized problem-solving workflows through the MCP protocol.

How to Use ConsultingAgents MCP Server: Multi-API Collaboration & Smart Solutions?

  1. Setup environment: Clone repository, configure virtual environment, and install dependencies via requirements.txt
  2. Configure authentication: Store API keys in .env file with proper variable naming conventions
  3. Initialize service: Execute startup script to launch server with debug capabilities if required
  4. Integrate with Claude Code: Register the server instance using absolute path mapping in the CLI configuration
  5. Operate via API: Trigger agent interactions through standardized POST requests or CLI commands

ConsultingAgents MCP Server Features

Key Features of ConsultingAgents MCP Server: Multi-API Collaboration & Smart Solutions?

  • Agent specialization: Dedicated processing pipelines for different AI workloads (reasoning/analysis/search)
  • Protocol adaptability: Supports both direct CLI interaction and HTTP RESTful API interfaces
  • Production-ready architecture: Fail-safe authentication handling and connection management modules
  • Extensible framework: Modular design allowing addition of new agents or protocol extensions
  • Diagnostic tooling: Built-in logging and health-check endpoints for operational transparency

Use Cases of ConsultingAgents MCP Server: Multi-API Collaboration & Smart Solutions?

Developers commonly utilize this framework in scenarios such as:

Complex Debugging

Concurrently analyze code issues with Darren's logical reasoning and Sonny's pattern recognition capabilities

Real-Time Documentation

Automate API reference lookups using Sergey's contextual search during development workflows

Multi-Perspective Analysis

Compare solution proposals from different AI models to validate algorithmic approaches

ConsultingAgents MCP Server FAQ

FAQ from ConsultingAgents MCP Server: Multi-API Collaboration & Smart Solutions?

Q: Why does the server fail to connect to Claude Code?

A: Ensure the server is running prior to initialization and the registered path matches the actual executable location

Q: How do I handle expired API keys?

A: Regenerate credentials in your provider dashboard and update the .env file immediately

Q: Can I use custom agents with this framework?

A: Yes, the modular API layer allows injecting new agents through the plugins directory following the defined interface

Content

ConsultingAgents MCP Server

A Model Context Protocol (MCP) server that allows Claude Code to consult with additional AI agents for code and problem analysis. This server provides access to Darren (OpenAI), Sonny (Anthropic), and Sergey (OpenAI with web search) as expert consultants, enabling multi-model perspective on coding problems.

Features

  • Darren : OpenAI expert coding consultant powered by o3-mini model with high reasoning capabilities
  • Sonny : Anthropic expert coding consultant powered by Claude 3.7 Sonnet with enhanced thinking
  • Sergey : OpenAI web search specialist powered by GPT-4o for finding relevant documentation and examples
  • MCP Integration : Seamless integration with Claude Code via MCP protocol
  • Multiple Transport Options : Supports stdio (for direct Claude Code integration) and HTTP/SSE transport

Prerequisites

  • Python 3.8+
  • OpenAI API key
  • Anthropic API key
  • Claude Code CLI (for integration)

Quick Start

  1. Clone the repository :

    git clone https://github.com/yourusername/consulting-agents-mcp.git

cd consulting-agents-mcp
  1. Create and activate a virtual environment :

    python -m venv mcp_venv

source mcp_venv/bin/activate  # On Windows: mcp_venv\Scripts\activate
  1. Install dependencies :

    pip install -r requirements.txt

  2. Set up API keys : Create a .env file in the project root:

    OPENAI_API_KEY=your_openai_api_key_here

ANTHROPIC_API_KEY=your_anthropic_api_key_here
  1. Start the server :

    chmod +x start_mcp_server.sh

./start_mcp_server.sh

Integration with Claude Code

  1. Register the MCP server with Claude Code:

    claude mcp add ConsultingAgents /absolute/path/to/consulting-agents-mcp/start_mcp_server.sh

  2. Start Claude Code with MCP integration:

    claude --mcp-debug

  3. Use the tools in Claude Code:

    Now you can use consult_with_darren, consult_with_sonny, and consult_with_sergey functions in Claude Code.

Available Tools

The MCP server provides three consulting tools:

consult_with_darren

Uses OpenAI's o3-mini model with high reasoning to analyze code and provide recommendations.

Parameters:

  • consultation_context: Description of the problem (required)
  • source_code: Optional code to analyze

consult_with_sonny

Uses Claude 3.7 Sonnet with enhanced thinking to provide in-depth code analysis.

Parameters:

  • consultation_context: Description of the problem (required)
  • source_code: Optional code to analyze

consult_with_sergey

Uses GPT-4o with web search capabilities to find relevant documentation and examples.

Parameters:

  • consultation_context: Description of what information or documentation you need (required)
  • search_query: Optional specific search query to use
  • source_code: Optional code for context

Advanced Configuration

Environment Variables

  • MCP_TRANSPORT: Transport protocol (default: "stdio", alternatives: "http", "sse")
  • HOST: Server host when using HTTP/SSE transport (default: "127.0.0.1")
  • PORT: Server port when using HTTP/SSE transport (default: 5000)

HTTP API (When Using HTTP Transport)

When running with HTTP transport, the server provides these endpoints:

Health Check

GET /health

Returns server status and available agents.

Model Consultation

POST /consult

Request body for Darren or Sonny:

{
  "agent": "Darren",
  "consultation_context": "I have a bug in my code where...",
  "source_code": "def example():\n    return 'hello'"
}

Request body for Sergey:

{
  "agent": "Sergey",
  "consultation_context": "How do I implement JWT authentication in Express?",
  "search_query": "express.js JWT auth implementation"
}

Troubleshooting

  • MCP Server Not Found : Verify the absolute path in your claude mcp add command
  • API Authentication Errors : Check that your API keys are correctly set in the .env file
  • Connection Issues : Ensure the MCP server is running before starting Claude Code
  • Debug Logs : Check the terminal where the MCP server is running for detailed logs

Development

Running in Development Mode

  1. Start the server with debug output:

    DEBUG=true ./start_mcp_server.sh

  2. Test HTTP endpoints (when using HTTP transport):

    Test Darren

curl -X POST http://localhost:5000/consult \
  -H "Content-Type: application/json" \
  -d '{"agent":"Darren","consultation_context":"Test message"}'

# Test Sonny
curl -X POST http://localhost:5000/consult \
  -H "Content-Type: application/json" \
  -d '{"agent":"Sonny","consultation_context":"Test message"}'

# Test Sergey
curl -X POST http://localhost:5000/consult \
  -H "Content-Type: application/json" \
  -d '{"agent":"Sergey","consultation_context":"Test message","search_query":"example"}'

Project Structure

  • mcp_consul_server.py: Main MCP server implementation
  • start_mcp_server.sh: Script to start the server with proper environment
  • requirements.txt: Python dependencies

License

MIT

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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