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Builds codebase dependency graphs so AI can analyze impact and modify safely.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "LensPR" yet — see the docs or source repo.
Using the current codebase dependency graph, analyze which services, APIs, and infrastructure configs would be affected if we change the authentication module, and group them by high, medium, and low risk.
A dependency-based impact analysis listing affected components and their risk levels.
I need to refactor the payment flow. Use the dependency graph to find the relevant code paths, upstream and downstream modules, and any configuration files that may need coordinated updates.
A structured list of code context related to the payment flow to help locate change points quickly.
First inspect the blast radius of this database model change using the codebase dependency graph, then suggest a safer modification plan and execution order.
An impact explanation and a safer step-by-step change plan to reduce modification risk.
Before refactoring a core module, developers can build a dependency graph to understand upstream and downstream relationships and estimate the impact scope. It is useful when coordinated changes span multiple languages or services.
When a team uses AI agents to analyze or edit code, this tool provides a structural view of the codebase to help the AI find relevant context. That makes dependency-aware modification suggestions more reliable.
For changes that involve both application code and infrastructure configuration, developers or DevOps engineers can use the dependency graph to inspect their relationships. This helps surface potential knock-on effects before making edits.
It is an MCP server that builds a dependency graph for a codebase. Based on the description, it gives AI agents structural understanding for impact analysis, context discovery, and safer code modifications.
The description says it works across languages and infrastructure. For more specific support details or limitations, see the source repository.
The provided material does not include installation steps, runtime requirements, or API key details. Please see the source repository for prerequisites.
Analyze codebases with semantic search, dependency insights, and natural language Q&A.
Give AI coding assistants memory, code graph insight, and safe multi-agent coordination.
Analyze any codebase and deliver structured, token-efficient context for AI assistants.
Search code semantically and answer questions about a codebase.
Semantic code graph search for coding agents to understand structure and dependencies.
Analyze, match, and transform code structures across multiple programming languages.