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Phone-a-Friend MCP Server 🧠📞

Phone-a-Friend MCP Server 🧠📞

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github.com/abhishekbhakatAI & Agents10Updated 69w ago

About Phone-a-Friend MCP Server 🧠📞

Phone-a-Friend MCP Server lets one AI consult an external AI for critical thinking, long-context reasoning, and complex problem solving. It packages context and selected files for analysis, then returns actionable insights or generates a prompt file for manual consultation.

Key features

  1. 01Send context and files for external AI analysis
  2. 02Extract actionable insights from responses
  3. 03Generate a prompt file for manual consultation
  4. 04Configure providers and models

Use cases

  1. 01Get another perspective on a complex multi-step problem
  2. 02Analyze long context or project files
  3. 03Consult another model on code or algorithm design
  4. 04Create a prompt file for a manual AI review

MCP support

Exposes consultation and prompt-file generation tools to MCP clients. The consultation tool calls external AI services through a configured provider.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Phone-a-Friend MCP Server 🧠📞

An AI-to-AI consultation system that enables one AI to "phone a friend" (another AI) for critical thinking, long context reasoning, and complex problem solving via OpenRouter.

The Problem 🤔

Sometimes an AI encounters complex problems that require:

  • Deep critical thinking beyond immediate capabilities
  • Long context reasoning with extensive information
  • Multi-step analysis that benefits from external perspective
  • Specialized expertise from different AI models

The Solution �

Phone-a-Friend MCP Server creates a two-step consultation process:

  1. Context + Reasoning: Package all relevant context and send to external AI for deep analysis
  2. Extract Actionable Insights: Process the reasoning response into usable format for the primary AI

This enables AI systems to leverage other AI models as "consultants" for complex reasoning tasks.

Architecture 🏗️

Primary AI → Phone-a-Friend MCP → OpenRouter → External AI (O3, Claude, etc.) → Processed Response → Primary AI

Sequential Workflow:

  1. analyze_context - Gather and structure all relevant context
  2. get_critical_thinking - Send context to external AI via OpenRouter for reasoning
  3. extract_actionable_insights - Process response into actionable format

When to Use 🎯

Ideal for:

  • Complex multi-step problems requiring deep analysis
  • Situations needing long context reasoning (>100k tokens)
  • Cross-domain expertise consultation
  • Critical decision-making with high stakes
  • Problems requiring multiple perspectives

Quick Start ⚡

Configure your MCP client (e.g., Claude Desktop) using the JSON block below—no cloning or manual installation required. The uv runner will automatically download and execute the server package if it isn't present.

Add the following JSON configuration to your MCP client and replace <YOUR_API_KEY> with your key:

{
  "mcpServers": {
    "phone-a-friend": {
      "command": "uvx",
      "args": [
        "phone-a-friend-mcp-server",
        "--provider", "openai",
        "--api-key", "<YOUR_API_KEY>"
      ]
    }
  }
}

That's it! You can now use the phone_a_friend tool in any compatible client. For more options, see the Advanced Configuration section.

Available Tools 🛠️

phone_a_friend

📞 Consult external AI for critical thinking and complex reasoning. Makes API calls to get responses.

fax_a_friend

📠 Generate master prompt file for manual AI consultation. Creates file for copy-paste workflow.

Parameters

phone_a_friend

  • all_related_context (required): General, non-code context such as constraints, tracebacks, or high-level requirements.
  • file_list (required): Array of file paths or glob patterns. Just pass the paths – the server automatically reads those files (skips anything in .gitignore or non-text/binary) and builds the full code context for the external AI.
  • task (required): A clear, specific description of what you want the external AI to do.

fax_a_friend

  • all_related_context (required): Same as above.
  • file_list (required): Same as above.
  • task (required): Same as above.
  • output_directory (required): Directory where the generated fax_a_friend.md master prompt file will be saved.

Advanced Configuration 🔧

This section covers all configuration options, including environment variables, CLI flags, and model selection.

Providers and API Keys

The server can be configured via CLI flags or environment variables.

ProviderCLI FlagEnvironment Variable
OpenAI--provider openaiOPENAI_API_KEY
OpenRouter--provider openrouterOPENROUTER_API_KEY
Anthropic--provider anthropicANTHROPIC_API_KEY
Google--provider googleGOOGLE_API_KEY

CLI Example:

phone-a-friend-mcp-server --provider openai --api-key "sk-..."

Environment Variable Example:

export OPENAI_API_KEY="sk-..."
phone-a-friend-mcp-server
Model Selection

You can override the default model for each provider.

ProviderDefault Model
OpenAIo3
AnthropicClaude 4 Opus
GoogleGemini 2.5 Pro Preview 05-06
OpenRouteranthropic/claude-4-opus

Override with CLI:

phone-a-friend-mcp-server --model "o3"

Override with Environment Variable:

export PHONE_A_FRIEND_MODEL="o3"
Additional Options
FeatureCLI FlagEnvironment VariableDefault
Temperature--temperature 0.5PHONE_A_FRIEND_TEMPERATURE0.4
Base URL--base-url ...PHONE_A_FRIEND_BASE_URLProvider default

Use Cases 🎯

  1. In-depth Reasoning for Vibe Coding
  2. For complex algorithms, data structures, or mathematical computations
  3. Frontend Development with React, Vue, CSS, or modern frontend frameworks

License 📄

MIT License - see LICENSE file for details.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.