$ loading_
Extract architecture, dependencies, and API knowledge from any codebase quickly.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Ferret MCP" yet — see the docs or source repo.
Analyze this codebase and summarize the system architecture, core module responsibilities, major dependencies, exposed APIs, and key design patterns.
A structured codebase overview with architecture, module summaries, dependency relationships, and an API inventory.
Based on the current codebase, identify the call chain, dependent components, and affected APIs around the authentication module, and assess the impact of changing login logic.
An impact analysis showing related modules, upstream and downstream dependencies, and high-risk change points.
Extract the tech stack, architectural patterns, external service dependencies, and API boundaries from the codebase, then produce a technical research summary suitable for onboarding new team members.
A newcomer-friendly technical summary that helps readers quickly grasp the project structure and key implementation approaches.
Help AI agents navigate, search, and understand codebases and change history.
Analyze codebases and generate runnable MCP servers from existing capabilities.
Build, debug, and manage software tasks with natural language across LLMs.
Give AI coding assistants memory, code graph insight, and safe multi-agent coordination.
Index codebases into Neo4j for analysis, dependency mapping, and impact assessment.
Search official library docs and return clean text ready for LLM use.