execute_tool
Execute an available FavCRM tool by name after discovering it with search_tools. Target tool availability, input schema, module gates, and audit logging are still enforced.
This record as markdown: /tools/io-favcrm-favcrm/execute-tool.md
What execute_tool does on FavCRM
AI agents invoke execute_tool to trigger actions in FavCRM. 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.
| Parameter | Type | Required | Description |
|---|---|---|---|
toolName | string | Yes | Exact target tool name returned by search_tools |
arguments | object | — | Arguments object for the target tool. Must match the target input schema. |
Parameters from the server's own tool schema.
Why execute_tool is rated High
This tool allows executing arbitrary FavCRM tools after discovery, which could include destructive operations (cancel_booking, cancel_subscription), financial actions (invoicing), or data modifications. The ability to execute tools dynamically based on runtime discovery bypasses static analysis and increases misuse risk.
From the tool's definition Tool name is 'execute_tool' and description states it 'Execute[s] an available FavCRM tool' — this is a meta-execution capability that runs other tools dynamically by name.
Attacks that exploit this kind of access
The rule that runs execute_tool safely
PolicyLayer is an MCP gateway: it sits between your AI agents and FavCRM, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For execute_tool, this is the rule to start with:
execute_tool stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect FavCRM, apply this rule, and every execute_tool call is checked against it from then on.
Questions about execute_tool
Execute an available FavCRM tool by name after discovering it with search_tools. Target tool availability, input schema, module gates, and audit logging are still enforced. It is categorised as a Execute tool in the FavCRM MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
execute_tool accepts 2 parameters: toolName, arguments. Required: toolName. The full parameter table on this page comes from the server's own tool schema.
Register the FavCRM MCP server in PolicyLayer and add a rule for execute_tool: 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 FavCRM. Nothing to install.
execute_tool 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 execute_tool 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 execute_tool. 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.
execute_tool is provided by the FavCRM MCP server (https://api.favcrm.io/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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