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Provides in-memory graph context and semantic code understanding for AI agents.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "MCP Context Graph" yet — see the docs or source repo.
Connect to MCP Context Graph, scan this project's codebase, and map the core modules, dependencies, and key call chains as a graph. Identify which files should be reviewed first.
A summary of the project structure, module dependency graph insights, and a prioritized list of files to review.
Using MCP Context Graph's semantic analysis, explain the main purpose of this repository, the responsibilities of its core components, and how they work together to complete business flows.
A developer-friendly codebase walkthrough covering system purpose, component responsibilities, and collaboration flow.
Use MCP Context Graph to maintain task-relevant code entities, relationships, and known conclusions for an AI agent, so it can reuse consistent context across multi-step work.
Structured context graph data that helps an AI agent preserve consistent understanding and reasoning across ongoing tasks.
Build a queryable code graph, validate edit scope, and log reasoning.
Connect AI to Graphlit for content access, retrieval, and context automation.
Provide persistent graph memory, semantic search, and traversal for AI agents.
Enable context-aware memory retrieval with authority weighting and conflict detection.
Query code structure and cross-language relationships via MCP with auditable access logs.
Analyze code structure and Git history while drastically reducing AI token usage.