McGravity

McGravity is a lightweight yet powerful MCP (Model Context Protocol) proxy that unifies multiple MCP servers behind a single, easily‑configurable endpoint. By routing client requests through McGravity, developers can treat several independent MCP services as one cohesive backend, eliminating the need to maintain separate connection logic for each tool or resource. This consolidation is especially valuable in environments where an AI assistant must tap into a diverse ecosystem of specialized models, data sources, or custom tooling without exposing the complexity to end‑users.

At its core, McGravity accepts a list of upstream MCP server URLs and forwards every request it receives to one of those servers. The proxy automatically mirrors the capabilities, resources, and prompts exposed by each target MCP, presenting a unified schema to clients. This design means that developers can add or remove underlying services simply by updating the configuration, without touching application code. The proxy also offers rudimentary load balancing, distributing traffic evenly across available backends to improve throughput and resilience.

Key features include a flexible configuration system (command‑line arguments or YAML files), customizable host and port settings, and the ability to specify MCP version and name metadata. The tool can be run as a standalone binary or via Docker, making it straightforward to deploy in CI/CD pipelines, containerized workloads, or local development setups. Its echo‑server example demonstrates how McGravity can be used to test and debug MCP interactions before integrating more complex services.

Real‑world scenarios for McGravity range from research labs that maintain multiple experimental models to production systems that need to scale out model serving across regions. For instance, a conversational AI platform might route general language queries to one high‑performance MCP while delegating domain‑specific tasks (e.g., legal or medical queries) to specialized servers. By exposing all these services through a single MCP endpoint, the platform can keep client integration simple while still leveraging diverse expertise.

In summary, McGravity solves a common pain point in modern AI workflows: managing many MCP backends without cluttering client code. Its proxy architecture, load‑balancing capability, and easy deployment options make it an attractive choice for developers who want to scale, orchestrate, or experiment with multiple model contexts in a unified, developer‑friendly manner.