Fetch one verified money fact with official source and as-of date. Pass q (e.g. "fdic insurance limit", "uk isa annual limit", "401k contribution"). Price: $0.01 USD USDC on Base via x402. Unpaid → HTTP 402. Unknown q → not_in_catalog (never invents).
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Short search query. Examples: "fdic insurance limit", "uk isa annual limit", "tfsa contribution", "fscs deposit protection". |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnly/openWorld/idempotent annotations already covering safety, the description adds substantial new behavior: the $0.01 USDC-on-Base payment model, HTTP 402 on unpaid requests, and the no-hallucination guarantee on unknown queries. These are exactly the traits annotations cannot express.
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?
Four tight sentences, each carrying new information (function, examples, price/status behavior, failure mode). No filler, well front-loaded.
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 covers when to call it, how to pay, what happens on failure, and the trust guarantee. No output schema exists, yet the agent has everything needed to invoke it and interpret outcomes.
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 coverage is 100%, so baseline is 3; the description nonetheless adds value by showing the kind of queries expected and reinforcing the short-query nature of q.
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?
States a specific verb+resource ('Fetch one verified money fact') with the distinguishing traits (official source, as-of date). It clearly differs from siblings like health_check, pricing_list, and scheme_lookup.
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?
Provides concrete example queries and tells the agent what happens on unknown input (not_in_catalog). It doesn't explicitly contrast with siblings, but the payment and failure semantics make the intended use clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.