Unichat MCP Server

Unichat MCP Server – A Unified AI Interaction Hub

The Unichat MCP server addresses a common pain point for developers working with multiple large‑language‑model (LLM) vendors: the friction of switching between distinct APIs, authentication flows, and request formats. By exposing a single, vendor‑agnostic MCP endpoint, the server lets an AI assistant (such as Claude) send a request once and receive responses from OpenAI, MistralAI, Anthropic, xAI, or Google AI without any additional plumbing. This unification simplifies integration into existing workflows and reduces the cognitive load of managing multiple API keys.

At its core, the server implements a single tool named . The tool accepts an array of conversational messages and forwards them to the selected LLM backend, returning the model’s reply. Behind the scenes it handles key rotation, model selection, and request formatting transparently. The server also ships a suite of ready‑made prompts that extend its utility beyond generic chat: code_review, document_code, explain_code, and code_rework. These prompts are designed for code‑centric tasks, allowing an assistant to audit best practices, auto‑generate documentation, explain logic step by step, or apply specified changes—all through a simple JSON payload. This is especially valuable for developers who need consistent, repeatable interactions with code across different LLMs.

Key capabilities include:

• Vendor‑agnostic routing – a single endpoint routes to the appropriate LLM based on environment variables, making it trivial to switch providers or models without changing client code.
• Predefined coding prompts – each prompt encapsulates a common developer workflow, reducing the need for custom prompt engineering.
• Secure API key handling – the server expects a single that can be mapped to any supported vendor, keeping credentials out of client logic.
• MCP‑compatible tooling – it conforms to the MCP specification, ensuring seamless integration with any client that supports stdio‑based MCP communication.

Real‑world scenarios where Unichat shines include:

• Continuous integration pipelines that need automated code reviews or documentation generation before merging.
• Developer IDE plugins that call LLMs for on‑the‑fly explanations or refactoring suggestions.
• Customer support bots that can query different LLMs based on the complexity of user queries or cost considerations.
• Education platforms that expose a single chat interface for students to interact with multiple AI tutors.

Integrating Unichat into an AI workflow is straightforward: configure the MCP client (e.g., Claude Desktop) to point at the server, set the and , and invoke either the tool or one of the predefined prompts. The server then handles all downstream communication, allowing developers to focus on business logic rather than vendor quirks. Its modular design also makes it easy to extend with additional prompts or tools, ensuring that the server can grow alongside evolving developer needs.