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Analyze Python scientific codebases with architecture, dependencies, tests, and AI-ready context.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "code-analysis-context-python-mcp" yet — see the docs or source repo.
Analyze the overall architecture of this Python scientific project. Describe the responsibilities of core modules, the call relationships between them, and identify areas with excessive coupling.
An architecture overview with module responsibilities, dependency relationships, and key areas that may need refactoring.
Identify common implementation patterns in this data analysis codebase, locate where pandas, NumPy, and SciPy are used, and generate a dependency relationship summary.
A pattern detection and dependency mapping report that helps quickly understand the data pipeline and stack distribution.
Assess the test coverage of this Python project, identify critical modules lacking tests, and provide a prioritized list of recommended test additions.
A test coverage analysis report with risk modules, gap explanations, and prioritized test improvement recommendations.
Turn any LLM into a codebase expert with search, mapping, and security insights.
Gives AI coding agents a structural map of your repository fast.
Analyze code structure and Git history while drastically reducing AI token usage.
Analyze codebases with semantic search, dependency insights, and natural language Q&A.
Index local repositories for semantic search and structured code understanding.
Analyze dependencies and fetch relevant docs to build project context fast.