Find the most common hidden cost in no-code automation: using polling where a webhook would do. Quantifies the wasted task/operation quota and the monthly dollars.
| Name | Required | Description | Default |
|---|---|---|---|
| platform | No | zapier or make (default zapier) | |
| workflows | Yes | JSON array of {name, triggerType: "polling"|"webhook", intervalMinutes, runsPerMonth, stepsPerRun, hasBranching?}. Accepts a JSON array, or a JSON string containing one. |
Changes observed during successful MCP inspections.
Input schema / properties / workflows / descriptionPrevious value: -"JSON array of {name, triggerType: \"polling\"|\"webhook\", intervalMinutes, runsPerMonth, stepsPerRun, hasBranching?}"New value: +"JSON array of {name, triggerType: \"polling\"|\"webhook\", intervalMinutes, runsPerMonth, stepsPerRun, hasBranching?}. Accepts a JSON array, or a JSON string containing one."Input schema / properties / workflows / typePrevious value: -"string"New value: +[
+ "string",
+ "array",
+ "object"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the behavioral transparency burden; it does disclose that the tool quantifies wasted quota and dollars, which is useful output behavior. However, it does not explicitly state that the operation is read-only, whether it only analyzes the supplied JSON locally, or what assumptions/limits apply to the calculation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two focused sentences with no filler; the core use case is front-loaded and the output is stated immediately. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is sufficient for a simple analysis tool with a well-documented schema: it tells the agent what problem to solve and what the result will quantify. There is no output schema, so a bit more detail about the return shape could improve completeness, but the current level is adequate for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters; the description adds valuable context about why the workflow fields matter (polling vs webhook waste) but does not add specific parameter-level meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Find') and a specific resource: the hidden cost of polling where a webhook would do in no-code automation. It also states the measurable outcome ('wasted task/operation quota and monthly dollars'), so an agent understands what the tool accomplishes. It does not name or contrast a sibling tool, so it lacks full differentiation but is clearly not tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly frames the intended scenario: no-code automation workflows with polling triggers where a webhook would be cheaper. This gives a clear usage context, but it does not explicitly state when not to use it or name alternatives such as compare_automation_platform_costs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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