Maps a local codebase's architecture, dependencies, impact areas, and file-level risk without API keys.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent โ or use 1-click editor setup below.
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๐ก Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by MCP Architect.
architecture_overviewLanguages, frameworks, ecosystems, size, topโlevel structure, entry points
dependency_graphInternal import graph, architectural hubs, **circular dependencies
impact_analysisWhat breaks if you change X** โ direct importers + transitive blast radius, hub risk
hotspotsLargest / most complex / mostโchanged (git) / highestโrisk files
explainDeepโdive a folder or file: classes, functions, external deps
The kannajune/mcp-architect MCP server gives an MCP client structured information about a local repository instead of requiring an assistant to infer architecture from a pasted file tree. It can identify languages, frameworks, ecosystems, repository size, top-level directories, and likely entry points. It also describes internal module relationships and provides summaries of the classes, functions, and external dependencies found in a selected file or folder.
The analysis is intended for architectural orientation and coding decisions. For example, an assistant can inspect which modules depend on a target before proposing a change, or find files that deserve review because they are large, complex, frequently changed, or otherwise risky.
The server runs on the developer's machine and reads the selected codebase locally. It does not send source code over the network, use telemetry, call an external API, or require an LLM. Python files receive full AST-based analysis. JavaScript and TypeScript imports are parsed for dependency information, while file and line-count statistics are available across more than 25 languages.
Its dependency and complexity results are heuristic. They are designed to provide fast situational awareness rather than replace a dedicated static-analysis system. The analysis package can also be imported independently as a Python library, including functions such as get_overview and get_dependency_graph.
Install the package with pip install mcp-architect, or let an MCP client retrieve and run it with uvx mcp-architect. A client configuration needs a server entry using the uvx command and mcp-architect as its argument. The README specifically documents compatibility with Claude Desktop, Cursor, Windsurf, and Cline.
Set MCP_ARCHITECT_ROOT in the server environment when tools should default to one fixed repository and callers should be able to omit paths. Otherwise, tools can receive repository paths directly. No API key, model download, or additional credential is required.
The kannajune/mcp-architect MCP server exposes these MCP tools:
architecture_overview reports languages, frameworks, ecosystems, repository size, top-level structure, and entry points.dependency_graph builds an internal import graph, identifies highly depended-upon modules, and checks for circular dependencies.impact_analysis lists direct importers, transitive change reach, and hub-related risk for a selected target.hotspots ranks files by size, complexity, Git change history, and calculated risk.explain examines a file or folder and summarizes its classes, functions, and external dependencies.Together, these tools support repository orientation, dependency review, change-impact questions, and prioritization of files for inspection.
The documented import-graph support is strongest for Python, JavaScript, and TypeScript. The roadmap lists Go, Rust, and Java import graphs as future work, so those languages should not be assumed to have equivalent dependency analysis. Layered-boundary checks, architecture comparisons, and optional local-LLM narratives are also listed as planned rather than current capabilities.
The project is released under the MIT license. Its runtime is described as using the MCP SDK plus Python standard-library analysis components, and the repository targets Python 3.10 or newer.
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