Get goal details by ID.
AI agents call get_goal to retrieve information from Agent Runtime without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves goal information from persistent storage without creating, modifying, deleting, or executing operations. It is a simple data query operation that has minimal blast radius if misused—an agent could retrieve unintended goal details but cannot alter system state or trigger external operations.
From the tool's definition Tool name 'get_goal' and description 'Get goal details by ID' indicate a retrieval operation with no modification or execution of side effects.
Attacks that exploit this kind of access
Get goal details by ID. It is categorised as a Read tool in the Agent Runtime MCP Server, which means it retrieves data without modifying state.
Register the Agent Runtime MCP server in PolicyLayer and add a rule for get_goal: 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 Agent Runtime. Nothing to install.
get_goal 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 get_goal 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 get_goal. 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.
get_goal is provided by the Agent Runtime MCP server (marc-shade/agent-runtime-mcp). 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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