AI agents call explain_fit to retrieve information from Crosswalk without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The tool purely analyzes and narrates career fit information, producing a markdown output. It has no side effects on stored data, does not execute code, and involves no financial transactions. It is a read/query-style operation with minimal blast radius.
From the tool's definition 'Produce a markdown narrative explaining fit, strengths, gaps, and positioning' — this is a read/analysis operation that generates explanatory text without modifying any data.
Attacks that exploit this kind of access
Produce a markdown narrative explaining fit, strengths, gaps, and positioning. It is categorised as a Read tool in the Crosswalk MCP Server, which means it retrieves data without modifying state.
Register the Crosswalk MCP server in PolicyLayer and add a rule for explain_fit: 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 Crosswalk. Nothing to install.
explain_fit 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 explain_fit 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 explain_fit. 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.
explain_fit is provided by the Crosswalk MCP server (mohakgarg5/crosswalk-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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