Comparr

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

Annotations already establish this as a safe read (readOnlyHint=true, openWorldHint=false), so the description is free to add the operationally important context: the freshness window (checked within 48h), the reference period (previous 90 days lows), the multi-reading requirement, and the new-condition/Amazon-only scope. That is meaningful beyond the annotations, though it says nothing about result volume or rate limits.

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 concept, and every clause in the first sentence carries a qualifying criterion an agent would otherwise have to guess. The first sentence is dense and clause-heavy, but nothing is padding; the two follow-up sentences on return payload and filtering are short and useful.

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 states what each deal returns (reference price, period, number of reading days), and it covers qualification criteria and filtering. Only edge details such as ordering, pagination or behavior when fewer deals exist than 'limit' are left unspecified.

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 both parameters are already documented; the description only restates the *optional* nature of the name filter and adds an example-free gloss ('words in the product name'), while never mentioning 'limit'. This meets the baseline of 3 without adding real semantics 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 resource ('Current verified price drops') and goes further by defining exactly what makes a drop qualify (same offer below its own 90-day low, multiple reading days, checked within 48h, new condition, Amazon re-verified). This clearly distinguishes it from get_price_history or search_products, though it never names those siblings explicitly.

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: an agent can infer this is the tool for pre-computed deal listings and that 'query' narrows by product name. There is no explicit when-to-use versus get_price_history or search_products, and no stated exclusions, so the agent must infer routing from the resource name alone.

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 openWorldHint=false, so safety is covered. The description adds valuable behavioral context beyond the annotations: data consists of periodic dated snapshots (often weekly) rather than daily tracking, and includes both aggregate daily best prices and per-offer series. It could go further on output volume or offer limits, but the core expectation-setting is strong.

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 tightly written sentences with zero waste. The core scope is front-loaded, followed by the distinguishing value and the granularity caveat. Every sentence 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?

For a simple read-only tool with two well-documented parameters and no output schema, the description explains the returned data content and its periodic granularity well. It does not cover pagination or response size, but nothing critical for correct invocation 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%, so both parameters (product, days) are fully documented in the schema. The description adds no syntax, default, or format detail beyond what the schema already provides, 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?

States a specific verb and resource: recorded price history of one product, including best daily price and per-offer series. The purpose is clear, but it does not explicitly differentiate itself from siblings like get_deals or get_product, leaving that 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?

Provides a rationale (telling a real price drop apart from a cheaper seller appearing) that implies when this tool is useful, but it gives no explicit when-to-use, when-not-to-use, or alternative-tool guidance against get_deals, get_product, or search_products.

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=true, openWorldHint=false), so the description's value is in disclosing the payload: price-check dates, exact seller, condition, and a computed buy-timing verdict. That is meaningful behavioral context, especially since there is no output schema. It omits error behavior for unknown slugs and any freshness/latency caveats.

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 that identifies the resource first, then enumerates the returned fields, all of which matter because there is no output schema. No filler or 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?

With one required parameter and no output schema, the description carries the burden of describing returns, and it does so with a concrete field list and the verdict's provenance ('computed from Comparr's recorded price history'). Minor gaps remain around error cases and result ordering/pagination.

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 the schema only says 'Product id or slug.' The description adds provenance for that value (obtained from search_products or get_deals), which is genuine meaning beyond the schema. Baseline would be 3; the sourcing hint lifts it.

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 ('All current offers for one product') plus the scope of the result set, which distinguishes it from the multi-item siblings get_deals and search_products. Sibling differentiation is implicit rather than explicit, but an agent can tell this is the single-product detail 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?

It tells the agent where the product id/slug comes from (search_products or get_deals), which is useful upstream routing, but it never says when to prefer get_deals or get_price_history over this tool. Usage is implied by the resource scope 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?

Adds substantial context beyond annotations: it discloses the return payload shape (ranked products, every offer, seller, condition, price-check date) and, importantly, the asynchronous behavior — a missing comparison is started and returns status 'pending' with retry_after_seconds, resolvable ~60s later. This also explains why readOnlyHint is false despite the read-like framing.

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 purpose, then layered with return detail and the pending/retry caveat. Information density is high with no filler, though the async clause is somewhat dense and could be tightened.

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 full burden and does so well: it describes the returned fields, the merchant coverage, and the pending-status edge case with a retry hint. Nothing essential for a correct call 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%, so the schema already documents query, limit and max_price. The description mirrors the query concept (category or specific model) but adds no syntax, format or currency nuance beyond what the schema provides; 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?

States a specific verb and resource — compares product prices across merchants — with geographic scoping (comparr.fr / comparr.co) that clearly separates it from sibling lookups like get_product and get_price_history. An agent can identify its role without opening the 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?

The description conveys when the tool is useful (comparing a category or a specific model across merchants), but never explicitly names the alternatives (get_product, get_price_history, get_deals) or states when NOT to use it. Usage is implied rather than prescribed.

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