AIMarket — live agent marketplace

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

With no annotations, the description carries full behavioral burden. It discloses external data source, current-value semantics, a signed receipt, per-call pricing, and a free-trial rule. It omits rate limits and error behavior, but the disclosed traits are meaningful and non-obvious.

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 tight sentences: result, input options, and cost. Information is front-loaded and no words are wasted.

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?

No output schema exists, so the description helpfully enumerates the returned metrics and units. It does not specify the exact response shape or behavior when neither city nor coordinates are given, but the core invocation contract is well covered.

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 baseline is 3. The description reinforces the 'city or coordinates' choice but adds no significant meaning beyond what the schema already documents.

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 clearly states the tool's outcome: current air quality at a place, with specific metrics (PM2.5, PM10, US and European AQI). It also names the data source, which distinguishes it from weather_now and nearby_sensors even without explicit sibling comparison.

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 gives explicit invocation guidance: pass latitude and longitude, or a city. It also adds cost and free-tier context that helps an agent decide when calling is appropriate. It does not explicitly name alternatives or exclusion conditions, but the intended use is clear.

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?

With no annotations, the description carries the full burden of behavioral disclosure. It does well by revealing determinism ('same seed always gives the same output'), verifiability, the proof mechanism, pricing ($0.006 per call), and the free tier. It does not specify the exact response structure, but the core behavior is transparent.

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, each earning its place: purpose and mechanism, determinism/fairness guidance, and pricing. The most important information is front-loaded, and there is no redundant or filler content.

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?

Despite having no output schema and no annotations, the description covers purpose, behavior, determinism, verification, and cost, which is enough for an agent to invoke the tool correctly. It could briefly mention the returned proof format, but the current level of detail is strong for a tool of this simplicity.

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 baseline is 3. The description adds meaningful context for 'seed' by explaining determinism and the need to publish it first, but it does not add new meaning for 'num_bytes' beyond the schema's default and range. This matches the baseline expectation.

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 purpose: producing verifiable random bytes for draws, raffles, or tie-breaks, via an ECVRF output with a checkable proof. It clearly differentiates from the unrelated siblings (air quality, market, weather tools) and explains the core function without ambiguity.

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 gives clear use cases ('for a draw, raffle or tie-break') and explains the important usage pattern of publishing the seed first to prove fairness. It does not explicitly name alternatives or exclusion criteria, but the sibling tools are unrelated, so this is sufficient context.

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?

With no annotations provided, the description carries the full behavioral burden and handles it well. It discloses trial allowances, signed receipts, the 402 fallback, the refusal to invent results, next_steps guidance, and the two payment mechanisms. This gives an agent a realistic model of tool behavior beyond a simple 'invoke' label.

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 dense but organized, front-loading the core purpose and then explaining the trial path, failure behavior, and paid alternatives. Every sentence contributes critical information, and there is no filler or repetition of schema field names beyond what is necessary for payment flow clarity.

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 complex tool with 11 parameters, no output schema, and no annotations, the description covers the main workflow, failure mode, and payment options well. It still leaves payment_authorization opaque and does not describe the shape of a successful response beyond mentioning a signed receipt, which prevents a perfect score.

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 description adds meaning to several schema-underspecified parameters by grouping payment_channel with its secret and pairing x_payment with x_payment_nonce. It also clarifies the trial/no-payment mode and the federated source_hub requirement. However, payment_authorization remains entirely unexplained in both schema and description, and the description does not map every parameter explicitly.

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 opens with 'Invoke a capability found via market_search', which states a specific verb and resource and explicitly ties this tool to its search sibling. It clearly differentiates market_invoke from market_search and the other environment tools by focusing on the invocation/payment act rather than data retrieval.

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 positions the tool as the follow-up to market_search by requiring product_id/capability_id/source_hub from search results. It also explains when trial invokes apply versus paid access, and contrasts the two payment paths. It does not explicitly state 'use market_search first' as a directive, but the dependency is strongly implied.

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?

No annotations are provided, so the description carries the burden of behavioral disclosure. It explains the search behavior ('by what you need, in plain words') and precisely describes the match result contents, including price and input fields. It does not mention auth, rate limits, or error behavior, but for a read-only search tool the disclosed behavior is sufficient.

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, efficient sentence with no filler. It front-loads the core action, then immediately states the output contract and relationship to market_invoke. Every clause 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 and no annotations, the description compensates by describing the key return fields and their purpose. It covers the main information an agent needs: search input style, result fields, pricing, and downstream invocation. It does not cover edge cases like empty results or exact matching behavior, hence not a perfect 5.

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 baseline is 3. The description adds a bit of context by tying results to market_invoke and explaining what `input` represents, but it does not meaningfully expand on parameter semantics beyond what the schema already documents.

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?

Description opens with a specific verb and resource: 'Search this hub's catalogue of live data and computation'. It clearly defines the tool's purpose and differentiates it from siblings like market_invoke by stating that results contain IDs to pass to market_invoke.

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 implies when to use the tool: when you need to discover marketplace offerings before invoking them. It names market_invoke as the follow-up tool, providing a usage context. It does not explicitly state when not to use it or mention alternatives, so it misses full explicit routing.

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?

With no annotations provided, the description carries the disclosure burden and does so well: it mentions pricing, the free-call tier, the one-sensor-per-layer behavior, and the output contents including the signed receipt. It does not cover empty-result or error behavior, but the key behavioral traits are disclosed.

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 compact, front-loaded with the core purpose, and every sentence adds value: result scope, output contents, pricing, and free tier. There is no filler or redundant restatement of the schema.

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?

Although there is no output schema, the description summarizes the return values well enough for an agent to understand what to expect. Input parameters are fully covered by the schema, and the description provides the essential behavioral and cost context. Explicit routing versus sibling tools would be a minor improvement.

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 the parameters are already well documented. The description adds useful semantic detail beyond the schema, especially the one-result-per-layer cardinality and example layer names, which clarifies how the layers parameter behaves.

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 operation: finding the nearest live public sensors to a coordinate, with one sensor per requested layer and each result including reading, distance, source, and receipt. This clearly distinguishes the tool from single-layer siblings like weather_now or air_quality_now by emphasizing multi-layer sensor 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?

The intended use is clear: when you have a latitude/longitude and need nearby live sensor readings across layers such as weather, air, radiation, or quake. It does not explicitly name alternative sibling tools or exclusion conditions, but the context is sufficiently clear for an agent to know when to invoke it.

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?

With no annotations provided, the description carries the full burden and does so well. It discloses the data source, radius, output units, signed receipt, per-call cost, and the free-first-5 pricing model. These are meaningful behavioral details beyond a generic 'get weather' statement.

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 sentences deliver the core behavior, parameters, constraints, and pricing with no filler. The most important information is front-loaded, and every clause adds value.

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 read-only weather lookup with three optional parameters and no output schema, the description covers input methods, output fields, measurement units, relay proximity, receipt, and pricing. Nothing essential is missing for an agent to invoke it correctly.

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 three parameters. The description adds the high-level instruction 'Pass latitude and longitude, or a city,' but does not introduce new semantics like precedence when both are supplied or city resolution behavior. This matches the baseline of 3.

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 clearly identifies the tool as returning current weather metrics (temperature, humidity, pressure, wind) for a place, and adds a concrete sourcing constraint (nearest live Open-Meteo relay within 75 km). This is specific enough to distinguish it from siblings like air_quality_now and nearby_sensors.

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 gives clear usage context: provide latitude/longitude or a city, and the tool resolves to the nearest relay within 75 km. It also mentions cost and a free tier, which helps callers decide when to call. It does not explicitly name alternatives or exclusion conditions, but the weather-specific wording makes the intended use unambiguous.

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