Peer Mcp – A Lightweight MCP Proxy for Local Development

Peer Mcp is a minimal, opinion‑less MCP proxy that exposes a locally running MCP server to external AI assistants. In many development scenarios, an assistant such as Claude or LlamaIndex needs to call out to a custom tool or data source that is only available on the developer’s machine. Peer Mcp solves this by acting as an intermediary: it listens for MCP requests from the assistant, forwards them to the local MCP instance, and returns the responses. This eliminates the need for complex networking setups or exposing sensitive services to the public internet.

The server’s value lies in its simplicity and flexibility. Developers can keep their tooling private while still enabling the assistant to leverage it during a session. Peer Mcp supports all core MCP capabilities—resources, tools, prompts, and sampling—so the assistant can discover available endpoints, invoke them with context‑aware arguments, and receive structured results. Because it operates over standard HTTP, any MCP‑compatible client can communicate with it without additional configuration.

Key features include:

• Transparent request routing – All MCP calls are forwarded verbatim, preserving headers and payloads.
• Local‑only exposure – The proxy binds to a local interface, ensuring that only trusted processes can reach the underlying server.
• Minimal footprint – Written in a lightweight language, Peer Mcp requires no external dependencies beyond the MCP core.
• Extensible middleware hooks – Developers can inject custom logic (e.g., logging, authentication) before forwarding requests.

Typical use cases involve debugging AI workflows on a laptop, testing new tool integrations in isolation, or prototyping conversational agents that need to access local databases or APIs. For instance, a data scientist might run a local model server that exposes a predictive API; Peer Mcp would let an assistant query this endpoint as if it were a cloud service, enabling rapid iteration on prompt engineering or tool design.

Integration into AI pipelines is straightforward: the assistant’s MCP client points to as its endpoint. The proxy then forwards calls to the actual MCP server running elsewhere on the machine (e.g., ). This pattern keeps the assistant’s codebase unchanged while granting full access to local resources, making Peer Mcp a practical bridge between local development environments and cloud‑based AI assistants.