competitor-ad-change-briefCompare supplied ad evidence and return deterministic changes, baseline state, and unsent actions. — $0.10/call, x402 (USDC on base).
| Name | Required | Description | Default |
|---|---|---|---|
| rows | No | ||
| maxAds | No | ||
| watchId | Yes | nike-us-demo-bootstrap | |
| coverage | Yes | ||
| datasetId | No | ||
| requestId | Yes | auto | |
| analysisModel | No | openai/gpt-4o-mini | |
| schemaVersion | Yes | 1.0 | |
| sourceContract | Yes | ||
| openrouterApiKey | No | ||
| maxTotalChargeUsd | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description reveals useful behavioral traits: 'deterministic' outputs and the cost/payment model ($0.10/call, x402). However, it does not disclose that the tool likely calls an external model (openrouterApiKey), whether any external state changes occur (readOnlyHint is false and openWorldHint is true), or how 'unsent actions' are handled. With annotations providing only limited safety info, the description adds some but not sufficient transparency.
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?
The description is a single functional sentence followed by pricing info. It is front-loaded with the primary action and output, contains no filler, and every clause adds meaningful information. Ideal conciseness.
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?
This is a highly complex tool with 11 parameters, nested objects, conditional allOf logic, a oneOf choice between rows and datasetId, and no output schema. The description covers none of this, leaving the agent to guess input structure, return format, and operational semantics. It is severely under-specified for its complexity.
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 0%; the description provides no explanations for any of the 11 parameters. The phrase 'supplied ad evidence' vaguely hints at the rows/evidence fields, but it does not help an agent understand the required coverage object, the rows-versus-datasetId oneOf, analysisModel, sourceContract, or pricing constraints. The complex conditional schema demands far more parameter guidance.
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 states a specific verb ('Compare') and resource ('supplied ad evidence') and explicitly names the three outputs: deterministic changes, baseline state, and unsent actions. This clearly differentiates the tool from sibling tools (app-release, pricing, map-retail), which operate in entirely different domains.
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 provides no guidance on when to use this tool over alternatives, no exclusions, and no context about typical scenarios or prerequisites. It only states what the tool does, leaving the agent to infer usage entirely from the tool name and schema.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct evidence domain (app reviews, ads, retail) and produces unique outputs. The only overlap is the general 'unsent actions' concept, but inputs and analysis types are clearly different.
Tool names are inconsistent: three use multi-word hyphenated phrases, while 'pricing_info' uses snake_case. There is no uniform verb-noun pattern or consistent stylistic choice.
Four tools is a reasonable count for a specialized server, though one tool is purely informational. The three functional tools cover distinct areas without feeling sparse or bloated.
Each analysis tool appears to cover its respective end-to-end workflow (evidence in -> findings/actions out). Minor gaps exist in broader decision-support capabilities, but for the declared bundle scope, the surface is fairly complete.