Bankrolled.ai Agent Hub

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint=false, so the safety profile is covered. The description nonetheless adds value the annotations don't: it discloses the return payload shape (ok, service, version, billed:false) and confirms no payment/authorization is needed.

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 clipped sentences, with the core action (liveness check) front-loaded and the cost/return facts following. Every clause earns its place and there is no filler.

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 zero-parameter liveness probe with no output schema, the description supplies exactly what is missing: the call is free and the response fields are enumerated. Nothing an agent needs to call it correctly is absent.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; the schema is empty and 100% covered. Baseline of 4 applies for a no-parameter tool.

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 gives a specific verb and resource ("Check Agent Hub liveness") and even names the exact return fields, so an agent knows precisely what the tool does. It never names a sibling, but the "(free)" / "No payment required" framing implicitly separates it from the payment-oriented siblings like pricing_list and money_fact.

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?

Usage is implied rather than stated: it tells the agent this is a free liveness probe that needs no payment, which is a real selection signal against paid siblings. However, there is no explicit when-to-use, when-not-to-use, or named alternative, so the agent must infer the calling conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly/idempotent/openWorld, but the description adds real behavioral context: it is free, it never invents facts and returns not_in_catalog instead, and it always attaches an official source with an as-of date. That is meaningful disclosure beyond the structured fields.

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?

Front-loaded with the core verb and return shape, and every clause carries information. The long fact_key enumeration duplicates the schema and costs some density, keeping it short of a 5.

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?

There is no output schema, and the description compensates by describing the response fields (source, as-of date, source_url, cite line) and the failure mode. Enough for an agent to call it correctly, though exact response structure remains slightly implicit.

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% and the schema already documents q plus all twelve fact_keys and 24 examples. The description's 'q = a fact_key or a short query' restates the schema rather than adding syntax or format detail, so the baseline 3 applies.

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 (Fetch) and resource (one verified money fact) plus the returned artifacts (official source, as-of date, source_url, cite line). It also names the sibling pricing_list, so an agent can distinguish it without opening a schema.

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?

Explicitly tells the agent what to pass (a fact_key or short query), enumerates the supported keys, defines the miss path (anything else → not_in_catalog), and routes to pricing_list for the full list of example queries. Both when-to-use and alternatives are covered.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish readOnlyHint=true, idempotentHint=true and a closed world, so the safety profile needs no restating. The description adds that all tools are free and documents the return shape (examples plus supported-queries list), which is useful, but says nothing about size, rate limits, or freshness of the listing.

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?

A single front-loaded sentence leading with the verb and resource, then specifics, then the docs URL. It is dense but every clause adds information; the only slight strain is the nested enumeration of fact_keys and countries.

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 zero-parameter, no-output-schema listing tool, the description covers what is returned (example arguments and the supported-queries list) and where to find more via the docs link. An agent would not be blocked by any missing detail, though return format specifics are only sketched.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate and the baseline of 4 applies. The description correctly does not invent parameter behavior.

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?

Clear verb ("List") and resource ("Agent Hub tools") with a precise statement of what the listing contains: example arguments plus the full supported-queries set covering money_fact fact_keys and scheme_lookup countries. It is distinguishable from the siblings by tying its contents to them, though it never explicitly frames itself as the discovery entry point vs. those siblings.

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?

Usage is implied rather than stated: the "full supported-queries list" wording signals this is the place to discover valid fact_keys and countries, and the docs link supports that. However, there is no explicit when-to-use or when-not-to-use instruction, and no guidance on how this relates to calling money_fact or scheme_lookup directly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations cover the safety profile (readOnly, openWorld, idempotent), and the description adds useful context beyond them: that the call is free, the provenance domain (bankrolled.com), and an included cite line. It stops short of noting rate limits or freshness guarantees.

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?

Front-loaded with cost and purpose, then scope, then parameters, with no filler. Densely packed but each clause (supported countries, default topic, example) 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?

With no output schema, the description usefully enumerates the return fields (name, coverage headline, official source, as_of, source_url, cite line), which is exactly what an agent needs. Only minor gaps around error/unsupported-country behavior remain.

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% and both parameters already carry enums, defaults, and an example, so the description largely restates structured data. It adds only marginal framing and no syntax beyond what the schema shows.

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 (look up) and resource (deposit-insurance scheme), then enumerates the returned fields, making the tool's scope unambiguous. It is clearly distinct from siblings health_check, money_fact, and pricing_list.

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?

It names the supported countries (US, UK, CA, AU, NZ) and the default topic, which constrains when the tool applies, but never states when to prefer it over a sibling or what to do for an unsupported country. Usage is implied rather than guided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.