Ask a live AI assistant a buyer question and report whether a given brand is named, at what position, and which competitors appear instead. Use for competitive and market research that reflects what assistants say right now. Costs $0.75 in USDC.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| aliases | Yes | ||
| domains | No | ||
| competitors | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose the $0.75 USDC cost and flags real-time, non-persistent characteristics ('live', 'right now'), which is genuinely useful. However, it does not disclose failure modes, whether the charge applies on failed calls, rate limits, or non-determinism caveats for a paid external-API tool.
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?
Three sentences with no filler: purpose, usage context, and cost. The main action is front-loaded, and 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?
For a paid tool with no output schema and no annotations, the description covers the core interaction and even implies the return content (brand named, position, competitors). Yet it leaves the 'domains' parameter unexplained and omits edge-case behavior (e.g., no brand mentioned, failed/incomplete assistant response, refund policy), which an agent would need for a reliable paid call.
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%, so the description must compensate. It implicitly clarifies 'prompt' as a buyer question, 'aliases' as the given brand, and 'competitors' as the alternatives to report. However, 'domains' is never mentioned or explained, leaving one of four parameters semantically undefined.
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 uses a specific verb+resource structure: 'Ask a live AI assistant a buyer question and report whether a given brand is named, at what position, and which competitors appear instead.' This clearly articulates the tool's function and output scope, and it distinctly separates the tool from its siblings (check_provenance, verify_claim, verify_source, pricing), which are verification/price-oriented rather than competitive-intelligence-oriented.
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 phrase 'Use for competitive and market research that reflects what assistants say right now' provides explicit context for when to invoke this tool. It implies the tool is not for historical fact-checking or source verification (as siblings are), but it stops short of naming explicit exclusions or alternative tools.
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 serves a distinct purpose: verifying claims, sources, answers, checking provenance, tracking waste, querying pricing, and asking an AI assistant. No two tools overlap in their intended use, making it easy for an agent to select the right one.
Tool names follow mixed conventions: three verify_* tools share a prefix, but others use different verbs (check_, report_) or noun forms (pricing, waste_report) and one reverses the verb pattern (answer_share). While individual names are descriptive, the lack of a uniform verb_noun structure makes the set less predictable.
With 8 tools, the count is well within the ideal range for a focused verification service. Each tool addresses a distinct need—claim, source, and answer verification, provenance checking, waste tracking, and pricing—without redundancy.
The surface covers all advertised types of verification (claim, source, answer) plus provenance checking and waste tracking. Minor gaps exist like no dedicated tool for bulk or historical verification, but the core workflows are fully represented.