MetaMCP

MetaMCP is a versatile MCP (Model Context Protocol) aggregator that lets developers stitch together multiple MCP servers into a single, coherent endpoint. By acting as both an MCP server and a proxy, it enables dynamic composition of capabilities—such as resources, tools, prompts, and sampling logic—from disparate back‑ends while preserving the familiar MCP interface for clients. This eliminates the need to modify existing AI assistants or client code; any tool that already speaks MCP can simply point to MetaMCP as its single source of truth.

The core problem MetaMCP solves is the fragmentation that occurs when an organization uses several specialized MCP services—for example, a private LLM server for sensitive data, a public API gateway for third‑party tools, and a local inference engine for low‑latency tasks. Without an aggregator, each client would have to maintain separate connections and reconcile differing schemas or authentication mechanisms. MetaMCP abstracts this complexity by exposing a unified namespace, automatically routing requests to the appropriate backend based on request metadata or custom rules. Middleware support further allows developers to inject cross‑cutting concerns such as logging, rate limiting, request transformation, or custom authentication flows without touching the underlying services.

Key features include:

• Dynamic aggregation: Combine any number of MCP servers at runtime, with hot‑reloading of configuration changes.
• Middleware stack: Chain reusable processing layers that can modify requests, responses, or enforce policies.
• Inspector endpoint: Expose a diagnostic interface that lists available tools, resources, and current middleware status for debugging.
• OpenID Connect (OIDC) integration: Support token‑based authentication from popular identity providers, enabling secure access control.
• Docker‑ready deployment: A single Docker image that bundles the proxy, middleware engine, and optional custom extensions.

Real‑world scenarios where MetaMCP shines include:

• Enterprise AI hubs: Centralizing internal LLMs, data‑privacy tools, and third‑party services under one gateway for compliance and monitoring.
• Hybrid cloud deployments: Seamlessly routing requests between on‑premise inference engines and cloud‑hosted APIs, balancing cost and latency.
• Rapid prototyping: Quickly swapping backend implementations (e.g., switching from a local model to an external API) without changing client code.
• Security‑first pipelines: Applying consistent authentication, audit logging, and request throttling across all connected MCP services.

By integrating MetaMCP into an AI workflow, developers gain a single point of entry that unifies diverse capabilities, simplifies client configuration, and provides extensible hooks for policy enforcement—all while maintaining full compatibility with existing MCP clients such as Claude, OpenAI’s tools, or custom agents.