The Bidit Registry

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds genuinely useful behavioral context by disclosing the full return-value space (exactly two states, no third) and naming the downstream fields offers_url, register_interest_url and consent_text, which no structured field provides since 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 compact sentences, front-loaded with the outcome and then the two states, with no filler. The parenthetical references to offers_url, register_interest_url and consent_text are dense and read awkwardly absent an output schema, which slightly blunts an otherwise tight structure.

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 single-parameter read tool with rich annotations, the description supplies the one thing structured data does not: the exhaustive set of return states and the meaning of each. It is complete enough to call correctly, though the sibling-boundary question (offer vs. price match) remains unaddressed.

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 exists and schema coverage is 100%, with the schema itself documenting that retailer accepts a name, domain or Registry slug. The description adds no additional syntax, format or constraint information beyond that, 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 (returns) and resource (whether a shopper can make an offer for this retailer) in the first clause, which is a real value proposition rather than a restatement of the title. It is not tautological, but it never names or contrasts against the sibling price-matching tools, so an agent must infer the boundary itself.

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 explains what the two possible answers mean and what each implies (Bidit offers vs. registering interest), which gives implicit guidance on how to act on the result. However, it says nothing about when to call this tool versus will_price_match or price_policy, so the routing decision is left to inference.

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/non-destructive, and the description adds substantial context beyond them: the 500-per-store catalogue ceiling, that prices are only as of last_seen_at, that 'accepts offers' is true only for stores in the live list, that price-match is only yes/no once a person verified the policy, and that coverage is partial. These caveats materially change how an agent should interpret results.

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?

One dense paragraph with the action front-loaded, followed by return contents, filters, and caveats. Very little waste, though the long parenthetical verification clauses make it heavier to scan than necessary.

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 still enumerates the returned fields (price, store page, last_seen_at, accepts_offers, price_match) so an agent knows what to expect, and the staleness and coverage caveats cover the remaining risk of misuse.

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, but the description adds real meaning: it explains that price_match only reflects verified policies and that accepts_offers is gated on a live list, and it frames country/price_max as filters over store currency and lowest price. The query parameter's semantics are left to 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?

States a specific verb and resource ('Searches the products the Registry has pulled from its retailers' own catalogues') and enumerates what each hit contains, which distinguishes it from siblings like retailers_that_price_match that return stores rather than products. The scope limit (Shopify stores, up to 500 products each) further pins down what it does.

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?

Gives clear context for calling it: searching catalogues by country, price ceiling, offers-only, or verified price-match. It also sets expectations ('confirm on the product page before quoting') and pre-empts a false negative ('an empty result is not a no'), but it names no sibling alternative for when a different tool is the right pick.

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 cover safety (readOnly, idempotent, non-destructive, closed-world), so the description is free to add genuinely new information. It does: the enumeration of what is included per retailer, and critically the null semantics ('Fields are null where nothing has been verified'), which prevents an agent from misreading absence as a negative policy.

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 sentence, front-loaded with the verb and resource, then a compact enumeration of covered areas, closing with the null-handling caveat. Every clause earns its place; nothing is repeated from the title or annotations.

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 carries the burden of describing the return surface, and it does so by naming the four content areas and explaining null values. It stops short of stating whether results are per-retailer unique or how unverified-vs-negative states are otherwise represented, but the essentials are present.

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, and schema coverage is 100% — the schema already documents 'retailer' as accepting a name, domain or Registry slug. The description adds no format or disambiguation detail beyond that, which is the expected baseline when the schema carries the parameter documentation.

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 clear verb+resource ('Returns everything the Registry has verified about how a retailer handles price') and enumerates the specific sub-domains covered (Bidit offers, price-match terms, promotions, clearance page). The breadth distinguishes it implicitly from the narrower siblings, but it never names them, so an agent must infer the boundary itself.

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?

No explicit when-to-use or when-not-to-use guidance, and none of the siblings (will_price_match, can_make_offer, retailers_that_price_match) are mentioned. The comprehensive framing implies this is the general lookup versus the targeted siblings, which is usable but left to inference.

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 cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint=false), so the bar is lower. The description adds genuinely non-structured context: that each entry carries terms and a policy page, and critically that a missing retailer means 'unverified, not a no' – a subtle interpretation rule the agent could not infer from annotations or 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?

Two sentences, zero waste. The scope and return shape are front-loaded, and the second sentence delivers the single most important interpretive caveat without padding.

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, read-only, no-output-schema tool, the description explains what is returned, how filtering works, and how to read an empty/partial result. That is close to complete; the only gap is the absence of explicit routing relative to its siblings.

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 country (ISO 3166) and category examples already documented in the schema itself. The description only restates that these filters are optional, adding no syntax or meaning beyond the schema, 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?

The description states a specific verb ('Returns'), a precise resource ('retailers whose price-match policy a person has verified'), and the shape of each result. It is clearly a registry/list lookup, which implicitly separates it from the judgment-oriented siblings (will_price_match, can_make_offer), though it never names them.

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 conveys that filtering is optional via category and country, so an agent knows it can call with no arguments, but it gives no explicit when-to-use guidance against the siblings like will_price_match or price_policy. The useful absence-semantics note is about interpreting results, not about selecting this tool.

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, etc., so the safety profile is clear. The description adds valuable context beyond annotations: it explains that verification is human-driven (yes = verified policy, no = human looked and found none, unknown = not verified), and that eligibility depends on returned terms. It doesn't mention rate limits or latency, but for a lookup tool this 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 two sentences, front‑loaded with the core purpose, then details the answer semantics and the dependency on terms. Every sentence earns its place; no waste.

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 no output schema, the description compensates by explaining the return shape (includes verified terms, exclusions, how to claim, quoted sentence, policy page). It clearly defines the three answer states and the conditional nature of qualification. Some gaps remain (e.g., response format specifics), but it is largely complete for an agent to invoke 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 coverage is 100%, so the baseline is 3. The description includes some parameter meaning: 'competitor_price' is echoed back with terms, and 'product' is what the shopper wants to buy. This adds marginal value over the already‑described schema, justifying a 4.

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: returns whether a retailer publishes a price-matching policy. It clearly distinguishes from siblings like 'retailers_that_price_match' (list vs. single lookup) and 'price_policy' (broader terms). The description leaves no ambiguity about what the tool does.

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 explains the three possible answer values and notes that qualifying depends on returned terms, which implicitly guides usage. However, it does not explicitly state when to use this tool versus siblings like 'price_policy' or 'retailers_that_price_match'.

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