Orbylon Readiness

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond the annotations: these tokens are closed-loop, non-cash, non-transferable, and the issuer is named in the answer.

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. The core action is front-loaded, the input is stated in the second sentence, and the final sentence adds an important restriction without bloating the description.

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 one-parameter, read-only lookup tool with strong annotations and no output schema, the description is complete enough. It explains what is returned ('what is left'), the token scope, and that the issuing business appears in the answer.

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%, with the 'code' parameter already explained via an example ('ABCD-2345-PQRS'). The description only restates that the tool takes 'the code printed on the card,' adding minimal semantic value beyond the schema.

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 ('Return what is left') on a specific resource ('closed-loop token issued by a business through Orbylon') and enumerates the token types it covers: gift card, prepaid hours, membership, or points balance. This clearly distinguishes it from siblings like lookup_business or place_order.

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 makes the triggering condition clear: use this when you have a code printed on a card and need to know its remaining value. It does not explicitly list when-not-to-use scenarios or name alternatives, but the resource and purpose are unambiguous enough for an agent to select it correctly.

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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful context beyond that: 'Prices are what the business published, not an estimate' clarifies the reliability and origin of the data. It also describes the output shape, which is useful given there is no output schema.

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: the first defines the return payload, the second clarifies price semantics, and the third gives the intended use case. 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?

For a simple one-parameter read-only tool, the description is nearly complete. It covers the return fields, price semantics, and usage context. It could mention handling of unverified domains or error behavior, but the annotations and schema cover the safety and parameter essentials.

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 schema already covers the single 'domain' parameter fully with a description and example. The description does not add new parameter-level information, so it meets the baseline for 100% schema coverage without needing to compensate.

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 ('Return') and names the resource ('machine-readable catalogue a verified business publishes'), and details the item fields. It clearly distinguishes this from reading prices off a website, and the scope ('verified business') sets it apart from sibling tools like lookup_business or list_verified_businesses.

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 context: 'Use this to quote a business accurately instead of reading prices off its website.' This states when to use the tool and identifies an alternative (manual website reading). It does not explicitly name sibling tools or list exclusion conditions, so it stops short of a full when/when-not guide.

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 mark the tool as read-only and idempotent, and the description adds significant context: returned URLs return HTTP 402 with payment terms, payment settles to the business, and failed calls are not charged. This goes well beyond the structured annotations.

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 dense sentences with no filler. The return fields are front-loaded, and the important payment-behavior caveat is stated compactly without repeating the tool name or title.

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?

Even with no output schema, the description enumerates the returned fields and explains the behavioral semantics of those returned endpoints, including the 402 response and no-charge-on-failure guarantee. For a one-parameter read-only listing tool, this is complete.

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 single domain parameter is already fully described in the input schema with a concrete example, and the description adds no additional parameter-level meaning. With 100% schema coverage, baseline 3 is appropriate.

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 ('Return') and a specific resource ('priced endpoints a verified business publishes'), and it names the exact fields returned. This clearly distinguishes it from sibling listing tools like list_business_offerings and list_verified_businesses.

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 purpose is obvious, but the description does not explicitly state when to choose this tool over alternatives or when not to use it. The distinction from list_business_offerings is implied rather than stated.

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 readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context beyond annotations: the ordering ('newest first') and the explicit return fields (public key, links to record and catalogue). This is valuable for the agent's expectations.

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 two sentences, no filler, with the main purpose and key details front-loaded. It earns its place by defining scope, ordering, and return content without redundancy.

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 simple list tool with one parameter and no output schema, the description adequately covers return content (public key, links) and ordering. The agent can infer the response structure from the description, and the single 'limit' parameter is self-explanatory. Nothing missing for correct invocation.

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?

Only one parameter, 'limit', is fully covered by the schema with a description ('How many to return, newest first. Defaults to 50.'). The tool description does not add extra semantics beyond the schema, but since schema coverage is 100%, the baseline of 3 is appropriate.

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 action (return businesses), the specific resource (verified, domain-bound identity on Orbylon), and the ordering (newest first). It also highlights key output details (public key and links), which helps distinguish it from generic list tools. This is distinct from siblings like list_business_offerings or lookup_business, which serve different purposes.

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 a clear when-to-use directive: 'Use this to find a business you can check cryptographically rather than one you have to take on trust.' This gives context for selection, though it doesn't explicitly name alternative tools or state when not to use it. That is a minor gap, but the guidance is still clear enough.

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 readOnlyHint=true and idempotentHint=true, covering safety. The description adds valuable behavioral context by explaining the semantics of a negative result: 'A not verified answer means Orbylon has no proof about them, not that they are fraudulent.' It also discloses what the record contains, enriching the agent's understanding beyond the structured annotations. No contradictions.

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 front-loaded with the main return value, then provides usage guidance, then clarifies negative-result semantics. Each sentence earns its place, though it is slightly longer than strictly necessary. 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?

Given there is no output schema, the description does an excellent job of explaining what the tool returns: the domain-bound Ed25519 public key, verification time/method, and provable capabilities (signed documents, catalogue). It also clarifies the meaning of not-verified results. With only one simple parameter and rich behavioral disclosure, nothing essential is missing.

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 single 'domain' parameter already has a description with an example. The description repeats the domain concept but adds no new format, validation, or usage details beyond the schema. Baseline 3 is appropriate.

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 ('Return') and resource ('verified identity record for a domain'), and enumerates the contents of the record (Ed25519 public key, verification timing/method, provable capabilities). This clearly distinguishes it from sibling tools like list_verified_businesses (which lists many) and check_business_token (which presumably checks a token rather than looking up a domain).

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 an explicit when-to-use directive: 'Use this before trusting or transacting with a business.' This is strong contextual guidance. It does not name alternatives or state when not to use it, so it misses the top tier for explicit when-not/alternatives, but the guidance is clear and actionable.

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 only indicate readOnlyHint=false and idempotentHint=false, so the description carries the behavioral burden. It richly discloses what happens: the order goes straight to the business, returns a reference and status URL, conditionally returns pay_url or book_url, and explicitly states money goes to the business and never to Orbylon. This goes well beyond the basic mutation hint.

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 yet dense, with the primary action and prerequisite front-loaded in the first sentence. Every subsequent sentence adds a distinct behavioral fact—routing, return values, payment handling, and appointment flow—with no filler or repetition of schema 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?

With no output schema, the description correctly takes responsibility for explaining return values, and it does: a reference, status URL, conditional pay_url, and book_url. It also covers the key trust-related behavior (money goes to the business) and the prerequisite link to list_business_offerings, making the tool safe for an agent to invoke without guessing.

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 71%, so most parameters are already documented in the schema. The description adds useful context about domain verification and the meaning of pay_url/book_url, but it does not materially explain the selection between item and item_id, or the fulfilment object's requirements beyond what the schema already states. It is adequate but does not compensate significantly for the undocumented parameters.

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 a specific verb-resource pair: 'Place an order for an item a verified business publishes', and immediately links to the sibling tool list_business_offerings for sourcing items. It clearly distinguishes this from the other business-list/readiness tools by framing it as the transactional ordering action.

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 context: it is used after a verified business publishes an offering, and directs the agent to list_business_offerings for the item source. It does not explicitly state when not to use this tool, but the direct reference to the prerequisite and the contrast with the read-only sibling tools makes the usage scenario fairly unambiguous.

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 readOnlyHint, idempotentHint, and destructiveHint=false, establishing the safety profile. The description adds behavioral context by specifying what the scan produces and that it operates on an externally supplied URL, implying an external call. It does not disclose potential latency, access requirements, or failure modes, but given the annotation coverage, this is acceptable value-added.

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 two concise sentences, front-loading the core purpose first ('Scan any business website...') and then listing the outputs. No filler or redundancy; every clause contributes meaning. It is efficient and immediately actionable.

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 a single parameter, no output schema, and strong annotations, the description adequately covers what an agent needs: it explains the scan's scope, the decision it supports (find, trust, pay), and the exact return format (pillar scores, grade, evidence, URL). No missing critical details for calling or interpreting results.

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% – the single parameter 'url' is fully described in the schema with a clear example ('example.com'). The description does not add any additional meaning or constraints about how to format the URL or handle edge cases, so it relies entirely on the schema, meeting 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 states a specific verb ('Scan'), a clear resource ('any business website'), and a precise outcome ('report whether AI agents can find, trust, and pay it'). It also enumerates the return payload (pillar scores, grade, evidence, report URL), making the tool unambiguous. This clearly distinguishes it from siblings like lookup_business (which presumably retrieves data) and list_* tools (which enumerate collections).

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 implies usage by stating 'Scan any business website', which suggests it applies broadly, but it does not explicitly state conditions, exclusions, or when to choose this tool over alternatives. It does not mention that siblings like check_business_token or list_business_offering might be more appropriate in specific contexts. Guidance is entirely implicit.

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