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by janreges • Tools & CLI
An MCP server that exposes AI Distiller functionality so AI agents (Claude, Cursor, etc.) can request distilled code structure and ready-made analysis prompts for large codebases.
Obtain a compact, AI-friendly representation of a repository's public API surface to avoid hallucinations when generating code.
Pre-built analysis prompts (security audits, refactoring suggestions, documentation workflows) combined with distilled code to run systematic assessments.
Browse and request dependency-aware, cross-file call-graph extracts so they can focus on only the code relevant to a specific entry point.
The MCP server provides access to AI Distiller's fast codebase "distillation" engine, which extracts public APIs, types, and minimal context from projects to fit into large-model context windows. It also generates AI-ready prompts and workflows (security, refactoring, documentation, deep file analysis) and supports dependency-aware cross-file tracing. The server integrates with MCP-compatible tools (Claude, Cursor, Gemini integrations via NPM) so agents can browse, distill, and retrieve structured outputs from within conversations or IDE workflows.
Extracts essential code structure from a single file - the core functionality of AI Distiller. Returns clean, structured code signatures optimized for AI context windows. USAGE: Essential for providing accurate code context to AI assistants. Automatically detects language and extracts API signatures, types, and structure while removing unnecessary implementation details.
Extracts essential code structure from entire directories - the core functionality of AI Distiller. Processes all supported programming languages and returns structured API/code signatures optimized for AI context windows. USAGE: Perfect for understanding codebases, API discovery, and providing accurate code context to AI assistants. Supports filtering by visibility levels, file patterns, and content types.
Generates a bug hunting prompt with distilled code for AI agents to systematically identify potential bugs, logical errors, race conditions, and quality issues. Use when you need AI to analyze code for hidden bugs or perform a comprehensive code health check. OUTPUT: Generates a markdown file with bug hunting prompt and distilled code. The response includes the file path - AI agents should read this file and follow instructions to perform the actual bug analysis.
Generates a refactoring analysis prompt with distilled code for AI agents to identify specific refactoring opportunities. Use when you need AI to suggest improvements for code quality, readability, maintainability, or performance. OUTPUT: Generates a markdown file with refactoring prompt and distilled code. The response includes the file path - AI agents should read this file and follow instructions to provide refactoring suggestions with before/after examples.
Generates a diagram creation prompt with distilled code for AI agents to create architectural diagrams in Mermaid format. Use when you need AI to generate flowcharts, sequence diagrams, class diagrams, and architecture overviews. OUTPUT: Generates a markdown file with diagram generation prompt and distilled code. The response includes the file path - AI agents should read this file and follow instructions to create 10+ Mermaid diagrams.
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