MCP Server Project

Overview

The MCP Server Project is a purpose‑built Model Context Protocol (MCP) server that exposes a secure HTTP API for executing AI assistant tools. It solves the common pain point of safely integrating external tool execution—such as file manipulation, shell commands, and LLM code generation—into conversational agents. By providing a single entry point that validates requests with JWT, sandboxed file operations, and rate‑limited authentication, developers can focus on building rich agent workflows without reinventing security or execution plumbing.

At its core, the server is built on FastMCP and Starlette, delivering fast asynchronous JSON‑RPC endpoints that adhere to the MCP specification. The tool catalog includes a full suite of file system utilities (create, read, list, write), a filtered shell executor that protects against arbitrary command injection, and two LLM code‑generation adapters for OpenAI and Gemini. Each tool is wrapped in a declarative configuration, making it trivial to add or disable capabilities through environment variables. This design allows agents to request precise operations—such as generating a Python script from natural language or creating a directory tree for a new project—while the server guarantees that no untrusted code can escape its sandbox.

Key capabilities of the MCP Server include:

• Secure authentication via JWT with configurable secrets and rate limiting on login endpoints.
• Path‑traversal protection for all file operations, ensuring agents can only touch files within a designated working directory.
• Shell command filtering that whitelists allowed commands and arguments, preventing accidental or malicious system changes.
• LLM adapters that abstract away API keys and request formatting for OpenAI and Gemini, enabling agents to invoke code generation without handling credentials directly.
• Observability through Prometheus metrics and audit logging, giving developers visibility into usage patterns and potential abuse.

Real‑world scenarios that benefit from this server include:

• Automated code review assistants that read repository files, run linters via shell commands, and generate fix suggestions through LLM adapters.
• Data‑pipeline builders that create directories, write configuration files, and trigger downstream scripts—all orchestrated by a conversational interface.
• Rapid prototyping tools where an agent writes, saves, and executes code snippets on demand while keeping the execution environment isolated.

Integrating the MCP Server into an AI workflow is straightforward: a client authenticates, obtains a JWT, and then issues JSON‑RPC calls to the endpoint. The server validates, executes the requested tool, and returns structured results that can be fed back into the agent’s context. Because all interactions are stateless and governed by the MCP spec, any AI platform that understands MCP—such as Claude or other LLMs—can plug in instantly, making this server a versatile bridge between conversational agents and the broader software ecosystem.