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Store, search, and track software decision traces with semantic retrieval.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Context Graph MCP Server" yet — see the docs or source repo.
Search Context Graph for past decisions related to using Redis as a caching layer, rank them by semantic similarity, and filter for records with successful outcomes.
A ranked list of relevant decision records including rationale, context, outcomes, and similarity scores.
Store this architecture decision in Context Graph: We decided to split the user notification service into a separate microservice to improve scalability and deployment independence; categorize it as architecture design; status adopted.
A structured decision record is created and saved with the decision, rationale, category, and status.
Analyze all decisions in Context Graph under the database selection category, summarize each current outcome status, and identify failed or unverified items.
A category-level summary showing successful, failed, or unverified decisions and highlighting key risk items.
Analyze and manage software architecture graphs for impact, dependencies, and design decisions.
Build, debug, and manage software tasks with natural language across LLMs.
Generate type-safe charts through MCP for AI apps using structured data.
Let AI agents store, search, and connect typed memories through MCP.
Index codebases into Neo4j for analysis, dependency mapping, and impact assessment.
Track structured reasoning with confidence, branches, and revisions for complex problem solving.