File Context Server

File Context Server – A Bridge Between LLMs and the File System

The File Context Server is an MCP (Model Context Protocol) service that equips large language models with direct, structured access to a project's file system. By exposing read, list, search, and analysis operations as MCP tools, it removes the need for custom file‑system adapters or insecure local code execution. Developers can now let an AI assistant pull in real‑time source code, run static analysis, and surface insights without leaving the chat interface or compromising host security.

At its core, the server offers three pillars of functionality: file operations, code analysis, and smart caching. The file‑operations toolset lets an LLM read arbitrary files, list directories with rich metadata (size, timestamps, permissions), and even watch for changes so that cached data stays fresh. The code‑analysis suite supplies metrics such as cyclomatic complexity, dependency graphs, and quality checks for duplicate or overly long lines. These metrics are delivered in JSON so the assistant can present concise summaries, highlight problematic functions, or suggest refactoring opportunities. The caching layer is highly configurable—using an LRU strategy with size limits, automatic invalidation on file changes, and the ability to cache recent search results—to keep response times low even for large codebases.

The server’s advanced search capability is another standout feature. It supports regular‑expression matching, multi‑pattern queries, file‑type filtering, and configurable context windows. By returning both the match and surrounding lines, an AI can offer meaningful explanations or pinpoint where a bug might originate. The search tool also respects exclusion patterns, making it suitable for large monorepos where certain directories (e.g., ) should be ignored.

In practice, the File Context Server shines in several scenarios. A developer working on a refactor can ask the assistant to “list all functions with complexity > 10 in the directory,” and receive a quick, actionable list. A QA engineer might request a search for all TODO comments across the repo and get them grouped by file. A continuous‑integration pipeline could invoke the server to run quality metrics before a merge, ensuring that code standards are enforced automatically. Because all interactions happen over MCP, the server can be deployed behind a firewall and accessed securely from any LLM that supports the protocol.

What sets this MCP server apart is its blend of real‑time file watching and cache invalidation, which guarantees that the AI always sees the latest state of the codebase without incurring redundant disk I/O. Coupled with detailed error codes and a flexible configuration via environment variables, it provides a robust, developer‑friendly bridge that scales from small projects to enterprise monorepos.