Invoke a deployed function and return its full HTTP response. Example — POST with body: Input: { app_id: "app_abc123", function_name: "submit-inquiry", body: { email: "user@example.com", message: "hello" } } Output: { status: 200, headers: { "content-type": "application/json" }, body: { id: "uuid...
Risk signalsAccepts raw HTML/template content (body)
Part of the Mcp server.
Free to start. No card required.
AI agents invoke invoke_function to trigger processes or run actions in Mcp. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
invoke_function can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"invoke_function": {
"limits": [
{
"counter": "invoke_function_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full Mcp policy for all 47 tools.
These attack patterns abuse exactly the kind of access invoke_function gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Invoke a deployed function and return its full HTTP response. Example — POST with body: Input: { app_id: "app_abc123", function_name: "submit-inquiry", body: { email: "user@example.com", message: "hello" } } Output: { status: 200, headers: { "content-type": "application/json" }, body: { id: "uuid-1234" }, duration_ms: 47 } Example — GET (no body): Input: { app_id: "app_abc123", function_name: "public-catalog", method: "GET" } Parameters: - method defaults to POST. The function's trigger config determines which methods are valid. - body is sent as JSON. Omit for GET/HEAD requests. - headers are merged with the default auth headers. Use this to: - Test a function immediately after deployment - Debug function logic with different inputs - Verify function response format and status codes Common errors: - RESOURCE_NOT_FOUND: Function doesn't exist, use manage_function (action: "list") to verify - Function timeout: Increase timeoutMs in deploy_function - Runtime error: Check manage_function (action: "get_logs") for stack trace Idempotency: Depends on function implementation (may have side effects).. It is categorised as a Execute tool in the Mcp MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the MCP server in PolicyLayer and add a rule for invoke_function: 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 Mcp. Nothing to install.
invoke_function 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 invoke_function 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 invoke_function. 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.
invoke_function is provided by the MCP server (@butterbase/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 47 Mcp tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.