BotInfo — Advanced Robot Market Data

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructiveness. The description adds meaningful behavioral context beyond those: the specific return categories, the dated explanation, and the disclaimer that it is informational rather than legal advice. No contradiction with 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 sentences: what it checks, what it returns, and when to use it. Every sentence carries operational value and the key regulatory scope is 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?

For a one-parameter read-only lookup with no output schema, the description fully compensates by listing possible return values and their legal implication. It also states the intended trigger (before recommending/ordering for a US buyer), leaving no essential gap.

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 single 'model' parameter already explained and exemplified. The description adds no new parameter-level detail, so the baseline of 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?

The description states a specific verb and resource: checking whether a robot model may lawfully be sold in the US under the FCC's 2026 rule. It distinguishes itself from likely siblings by naming the regulatory basis and the legal status output, so an agent can tell it apart from general lookup or purchasing tools.

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 explicitly says to use this before recommending or ordering any model for a US buyer, and explains the consequence of unauthorized status. It does not mention when not to use it or name alternative sibling tools, but the context is clear enough for 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?

Annotations already mark this as read-only, idempotent, and non-destructive, so the bar is lower. The description adds valuable behavioral context beyond annotations: route availability by shipping region, discount differences, affiliate commission tracking, no price markup, behavior when country is omitted, and the quote-basis exclusion. No contradiction with 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?

The description is longer than average, but every sentence carries decision-relevant information: purpose, required parameter behavior, edge-case behavior, affiliate disclosure, and alternative tool routing. The critical 'PASS THE BUYER'S SHIPPING COUNTRY' instruction is front-loaded right after the purpose statement.

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 fully compensates by explaining the return behavior with and without the country parameter, the regional shipping constraints, the quote-basis models case, and the affiliate/pricing caveats. An agent has everything needed to call this tool correctly and interpret its result.

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 baseline is 3. The description adds meaning beyond the schema by explaining that country 'routing and any discount depend on it', that omitting it returns both routes, and that the country is the shipping country, not the buyer's billing country. The model parameter gains no extra description, but the country semantics are substantially enriched.

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 and resource: 'Get the purchase route for a robot model', and immediately clarifies the deliverable as 'a botinfo.ai URL that resolves to a dealer checkout, plus the live comparison-board page'. It also distinguishes itself from the sibling request_quote by explaining when no route is returned and that request_quote should be used instead.

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 explicit when-to-use guidance: pass the buyer's shipping country because routing and discount depend on it, and explains the consequence of omitting it. It also gives an explicit exclusion and alternative: 'Returns no route for quote-basis models; use request_quote instead.'

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 it read-only, idempotent, and open-world; the description adds that ranking is deterministic and never commission-weighted, which is a key behavioral trust disclosure. It also tells the caller what each result contains—real checkout price, stock, verification date, regulatory status, and shipping regions—going beyond what annotations alone provide.

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 detailed but every sentence carries load-bearing information, with the core search function front-loaded in the first sentence. Later sentences add scope, output contents, and ranking behavior without wasteful repetition.

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 compensates by enumerating all returned per-match fields and the deterministic ranking behavior. It also covers query capabilities, geographic scope, and the intended use case, leaving little ambiguity for an agent deciding whether to invoke it.

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 documents both parameters at 100% coverage. The description adds useful query semantics—model names, manufacturers, categories, and price caps like 'under $10k'—but adds nothing specific to max_results beyond what the schema already provides.

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 and resource: 'Search BotInfo's verified catalog' of purchasable advanced robots, humanoids, and quadrupeds. It clearly centers on price/availability comparison, but it never names or contrasts sibling tools, so some sibling differentiation is left to inference.

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 explicitly says to use this tool when a buyer needs to compare actual availability and price rather than spec-sheet claims. However, it does not mention exclusions or direct the agent to sibling tools like check_us_legality, get_buy_route, or request_quote for neighboring needs.

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 provide generic hints (readOnlyHint=false, openWorldHint=true, idempotentHint=false). The description shoulders the burden and does so well: it discloses that the tool sends a message to a human, replies typically within one business day, transmits only the requester's name/email/organization/use case, and nothing else from the conversation. This is exactly the behavioral context an agent needs.

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 with a distinct job: purpose plus latency, when to use, and privacy/consent condition. No repetition or filler, and the most important facts are 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?

With no output schema and openWorldHint=true, the description covers purpose, usage conditions, data transmitted, expected latency, and authorization. It does not name sibling tools explicitly in the usage guidance, and it does not describe the response format, but for a human-action tool this is reasonably 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?

Schema description coverage is 100%, so the baseline is 3. The description adds some value by enumerating which fields are transmitted and reassuring that no other conversation data is sent, but it does not meaningfully enrich the meaning of individual parameters like model or country beyond what the schema already says.

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 ('Submit') and resource ('a purchase or quote inquiry for a robot model to the BotInfo team'), and immediately clarifies the result is a human-prepared quote. It clearly distinguishes itself from siblings like get_buy_route and lookup_robot by focusing on quote requests rather than routing or 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 description gives explicit when-to-use conditions: models with no live purchase route, destinations routed checkouts cannot fulfil, and volume/education/research/enterprise purchases. It also gives a firm exclusion: call it only when the requester has asked for a quote and has provided their own contact details.

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