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Expose a CognOS agent system as a JSON graph for full inspection.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Conductor Graph MCP Server" yet — see the docs or source repo.
Read the current CognOS agent system JSON graph, review all nodes, edges, and blocked nodes, and summarize the current system state in English.
A system graph overview with node count, connections, blocked nodes, and an overall status summary.
Inspect all blocked nodes in the graph, list their names, upstream dependencies, and possible blocking causes, then rank them by priority.
A list of blocked nodes, dependency analysis, and a recommended remediation priority order.
Using the JSON graph, analyze the upstream and downstream relationships of a specified node, explain what it affects and depends on, and identify potential risk points.
A dependency chain, impact scope, and potential failure propagation risks for the specified node.
Build a queryable code graph, validate edit scope, and log reasoning.
Provide persistent graph memory, semantic search, and traversal for AI agents.
Ingest documents into Neo4j to build and query a knowledge graph.
Index codebases into Neo4j for analysis, dependency mapping, and impact assessment.
Enable AI agents to query knowledge graphs and delegate shell tasks efficiently.
Query code structure and cross-language relationships via MCP with auditable access logs.