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Analyze Python call graphs to understand dependencies and code impact quickly.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "pyscope-mcp" yet — see the docs or source repo.
Analyze the call graph of the function process_order in the project. List its callers and callees, and present them by hierarchy.
A hierarchical list of callers and callees for process_order, helping clarify upstream and downstream dependencies.
I plan to modify the module payments/refund.py. Use the call graph to find neighboring functions and modules, and summarize the likely impact scope.
A list of neighboring call-graph nodes and the likely impact scope for pre-change risk assessment.
Use call graphs to provide an overview of this Python project's core modules, highlighting key entry functions, major dependency paths, and highly connected nodes.
An overview of the code structure and key call paths, helping users learn the project architecture faster.
Analyze codebases with metrics, thresholds, and quality checks for faster reviews.
Statically analyze Python code structure and dependencies without running the code.
Index codebases into Neo4j for analysis, dependency mapping, and impact assessment.
Analyze PyPI packages for dependencies, security, licenses, health, and trends.
Help AI agents navigate, search, and understand codebases and change history.
Connect to and operate MCP servers from the command line.