FileScopeMCP: Deep Insights into Your Codebase

FileScopeMCP addresses the common developer challenge of navigating and understanding large, multi‑language codebases. It analyzes a project’s directory structure to identify which files are most critical, maps how those files depend on one another, and offers concise summaries that can be consumed by AI assistants. By exposing this information through the Model Context Protocol, developers can equip Claude or other agents with an instant mental model of their code, dramatically accelerating onboarding, debugging, and refactoring tasks.

The server performs a comprehensive scan of the target directory, supporting languages such as JavaScript/TypeScript, Python, C/C++, Rust, Lua, Zig, C#, and Java. For every file it calculates an importance score on a 0‑10 scale, taking into account the number of incoming and outgoing dependencies, file type, location in the tree, and name semantics. This ranking lets users focus on the “core” files that drive the application’s behavior while quickly spotting peripheral or legacy modules.

Dependency tracking is bidirectional: the tool records which files import a given file (dependents) and which files it imports (dependencies), distinguishing local modules from external packages. The data is then rendered into interactive Mermaid diagrams—color‑coded by importance, with optional depth limits and filters. These visualizations can be embedded in HTML reports or displayed directly within an AI‑powered IDE, giving developers a live map of the code’s architecture.

FileScopeMCP also supports custom summaries. Users can annotate any file with a human or AI‑generated description, which is stored persistently in JSON. When queried through MCP, an assistant can retrieve these summaries to provide instant context about a file’s purpose without opening it. The server manages multiple “file trees” for different project areas, caching results to avoid rescanning and enabling rapid context switching between subsystems or modules.

In practice, developers integrate FileScopeMCP into their workflow by configuring it in a Cursor file. Once running, an AI assistant can ask questions such as “Which files are most critical for the login flow?” or “Show me a dependency graph of the payment module.” The assistant can then return a ranked list, a Mermaid diagram, or a stored summary, allowing the developer to focus on high‑impact changes, spot architectural smells, or onboard new team members with minimal friction. The combination of automatic importance scoring, bidirectional dependency mapping, and persistent summarization makes FileScopeMCP a powerful tool for anyone looking to tame complex codebases with AI assistance.