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Enable AI agents to query knowledge graphs and delegate shell tasks efficiently.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-graphify-autotrigger" yet — see the docs or source repo.
Use mcp-graphify-autotrigger to analyze the current codebase knowledge graph, identify services, config files, and key scripts used by the auth module, and list their relationships hierarchically.
A structured result showing dependency nodes, related files, and call relationships for the auth module.
Through mcp-graphify-autotrigger, delegate this task to the shell: check Docker container status, inspect the last 200 log lines, and summarize abnormal services.
Outputs container status, key log summaries, and a list of abnormal services with an initial diagnosis.
Use mcp-graphify-autotrigger to query the knowledge graph first, then return only the minimal context relevant to 'payment failure investigation' instead of sending the whole repository to the model.
Provides compact but sufficient troubleshooting context, reducing token usage and improving agent response efficiency.
Turn codebases into structural graphs for efficient AI-assisted code exploration.
Provide persistent graph memory, semantic search, and traversal for AI agents.
Build a queryable code graph, validate edit scope, and log reasoning.
Adds nodes, edges, and semantic retrieval to agent knowledge graphs.
Build AI agent workflows and automate tasks using MCP-connected services.
Use natural language to query TigerGraph, run GSQL, and manage graph data.