graph_query
AI agents call graph_query to retrieve information from Chef-Agent without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
A 'graph_query' tool without additional context most likely performs read operations against the Neo4j graph to retrieve recipe data, user preferences, or cooking knowledge. The sibling tools (execute_python, ingest_url_to_graph, web_scraper, web_search) suggest this server handles data ingestion and retrieval.
From the tool's definition Tool name 'graph_query' suggests querying a Neo4j knowledge graph for retrieval purposes. Description is empty, limiting certainty.
Attacks that exploit this kind of access
graph_query. It is categorised as a Read tool in the Chef-Agent MCP Server, which means it retrieves data without modifying state.
Register the Chef-Agent MCP server in PolicyLayer and add a rule for graph_query: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Chef-Agent. Nothing to install.
graph_query is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the graph_query rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for graph_query. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
graph_query is provided by the Chef-Agent MCP server (omnagvekar/chef-agent). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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