AI agents invoke send_request to trigger actions in Req. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool executes arbitrary HTTP requests against external endpoints. It supports session-aware operations including cookie persistence and bearer token reuse, meaning a misused invocation could trigger state-changing operations (POST, PUT, DELETE) on real APIs with authenticated sessions.
From the tool's definition "Sends an HTTP request using the current Request API session"
Attacks that exploit this kind of access
Sends an HTTP request using the current Request API session. It is categorised as a Execute tool in the Req MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Req MCP server in PolicyLayer and add a rule for send_request: 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 Req. Nothing to install.
send_request is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the send_request 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 send_request. 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.
send_request is provided by the Req MCP server (tarminarx/request-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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