Consulting Agents MCP Server

The Consulting Agents MCP Server addresses a common pain point for developers building AI‑enhanced tooling: the need to tap into multiple advanced language models without juggling separate APIs, credentials, or workflows. By exposing a unified Model Context Protocol endpoint, the server lets Claude Code (or any MCP‑compatible client) request expert analysis from four distinct AI agents—each backed by a different provider and specialty. This multi‑model approach gives developers the flexibility to choose the best tool for a given task, compare perspectives side‑by‑side, and aggregate insights into a single conversational context.

At its core, the server hosts four consulting tools:

• – an OpenAI‑powered consultant built on the o3-mini model, optimized for deep code reasoning and debugging suggestions.
• – an Anthropic‑based agent using Claude 3.7 Sonnet, offering a second opinion from another Claude architecture with enhanced “thinking” capabilities.
• – a GPT‑4o specialist that can perform web searches, pulling in up‑to‑date documentation or example snippets to inform the discussion.
• – a Google Gemini 2.5 Pro model equipped with a massive 1 M‑token context window, ideal for comprehensive repository analysis and large codebase reviews.

Each tool accepts a concise set of parameters (e.g., , optional , or a search query) and returns structured, model‑specific responses that can be consumed directly by the client. Because the server adheres to MCP standards, developers can plug it into Claude Code via a simple command and start invoking these helpers with native tool calls. The server also supports both stdio (for tight CLI integration) and HTTP/SSE transports, giving teams flexibility in how they expose the service within their infrastructure.

Real‑world scenarios that benefit from this server include:

• Code review automation – a single prompt can dispatch to multiple agents, gathering diverse recommendations before consolidating them into a final review.
• Rapid prototyping – developers can ask each model to generate sample implementations, compare style and performance suggestions, and select the most suitable snippet.
• Documentation & learning – Sergey’s web‑search capability can surface relevant docs or tutorials, while Gemma’s large context window can digest an entire repository and explain architecture to newcomers.
• Hybrid compliance checks – by leveraging different provider policies, teams can cross‑validate outputs for safety and bias concerns before deployment.

In summary, the Consulting Agents MCP Server transforms a fragmented AI landscape into a single, coherent API surface. It empowers developers to harness the strengths of multiple leading models—OpenAI, Anthropic, and Google—in a streamlined workflow that enhances code quality, accelerates problem solving, and provides richer insights than any single model alone.