AI agents call score_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.
This tool reads stored resume data and job information to compute a fit score with strengths/gaps analysis. It only retrieves and processes existing data without creating, modifying, or deleting anything. Pure read/query operation with no side effects.
From the tool's definition Score a job against a stored resume. Returns numeric score + structured strengths/gaps.
Attacks that exploit this kind of access
Score a job against a stored resume. Returns numeric score + structured strengths/gaps. 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 score_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.
score_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 score_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 score_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.
score_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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