This returns a filtered set of subawards
AI agents call subawards to retrieve information from Usaspending without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The tool fetches subaward records from USASpending.gov based on filters. It is a read-only query operation with no write, execute, or destructive capabilities. Misuse risk is minimal as it only retrieves public government spending data.
From the tool's definition 'This returns a filtered set of subawards' — purely retrieves/queries data with no side effects
Attacks that exploit this kind of access
This returns a filtered set of subawards. It is categorised as a Read tool in the Usaspending MCP Server, which means it retrieves data without modifying state.
Register the Usaspending MCP server in PolicyLayer and add a rule for subawards: 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 Usaspending. Nothing to install.
subawards 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 subawards 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 subawards. 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.
subawards is provided by the Usaspending MCP server (thsmale/usaspending-mcp-server). 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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