Pechincha.ai

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

Annotations already declare readOnlyHint=true, openWorldHint=false and destructiveHint=false, so the safe-read profile is covered by structured data. The description adds some behavioral value by describing the returned payload and the negative case ("or says there is none"), plus the Portuguese-language output note, though return details are largely duplicated by the 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?

Two tightly written sentences with zero filler, front-loaded with the core question before describing the return payload. Every clause earns its place and nothing is repeated.

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 full input schema, output schema and safety annotations, the description covers what the tool does, what it returns, and the no-result case adequately for a single-parameter read tool. The only shortfall is the absence of guidance on when to prefer sibling tools.

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% for the single url parameter, so the schema already carries the semantics. The description's phrase "product page URL from an online store" largely restates the schema rather than adding format or validation nuance (e.g., must be a specific store's product page, not a category or redirect URL).

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 gives a specific verb+resource ("check whether ... has an active deal for a product page URL"), which is clear and actionable. It implicitly distinguishes itself from ID-based siblings like get_product and get_deal by keying on a store URL, but it never names or contrasts those siblings explicitly, so it stops short of the top score.

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 only implied: the agent can infer you call this when you have a product page URL and want to know if a deal exists. There is no explicit when-to-use vs. when-not, no statement of prerequisites, and no routing to alternatives such as get_product or get_deal when a URL is not available.

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 declare readOnlyHint=true, openWorldHint=false, destructiveHint=false, so the safety profile is covered. The description adds the output format and language fact ('Markdown in Brazilian Portuguese'), which is useful behavioral context. It doesn't mention authentication requirements, rate limits, or latency, so it's adequate but not rich.

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?

Very compact single sentence that front-loads the tool's purpose and enumerates the return payload. No filler. One sentence for a tool this simple is appropriate.

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?

Output schema exists, so return-field details need not be re-explained; the description's enumeration is a helpful summary. Annotations cover safety. What's missing is invocation context: when this tool is the right call versus get_product or get_price_history, and whether the id is required (schema covers that). Complete for the call itself, slightly thin for selection.

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 id parameter is fully documented in the schema. The description adds no parameter-level meaning beyond that. Baseline 3 applies when schema carries the burden.

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+resource ('Full details of a deal') and enumerates what is returned (price, discount, store, coupon, link, history, pros/cons, specs, FAQ). It doesn't explicitly differentiate from siblings like get_product or get_price_history, so it stays below 5.

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 statement of when to use this tool versus alternatives. The sibling get_product and get_price_history overlap in content, and an agent has no guidance on which to prefer. The description provides no conditional logic.

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 (readOnly, not destructive, closed-world), and the description adds real behavioral context on top: the shape of the returned summary, an update-frequency-independent "insufficient history" signal under 3 distinct days, and the fact that all text is Brazilian Portuguese. It does not mention rate limits or latency, but this is well beyond the annotation baseline.

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-loading the resource and purpose before listing outputs and edge-case behavior. Slight awkwardness in "Says so when..." (the agent as subject is unstated), but no sentence is 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?

With an output schema present, the description needn't enumerate return values, yet it summarizes them helpfully for selection; annotations cover safety and the schema covers the required param. The only real gap is that period's semantics (what each window means) are left to the enum alone.

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 50%: product_id is fully documented in the schema (including how to extract it from the URL), while period is undocumented beyond its enum and default. The enum values 30d/90d/1y are largely self-explanatory, but the description adds no meaning for the window or its default, so 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+resource ("Price history of a product") and the analytical intent ("to judge whether a discount is real"), which is unambiguous and distinct from reading a single current price. It stops short of naming how it differs from siblings like get_product or get_deal, so 4 rather than 5.

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?

"to judge whether a discount is real" implies the scenario the tool serves, and the caveat about needing 3+ distinct days effectively signals a limitation. However there is no explicit when-to-use-vs-alternative routing against get_product or get_deal, leaving usage 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 confirm this is a safe read operation, so safety burden is lifted. The description adds useful context about the output format (Markdown in Brazilian Portuguese), which isn't in annotations, but doesn't cover latency, caching, or freshness of data.

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, dense sentence that front-loads the return contents and tacks on format information. 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?

An output schema exists, so return values needn't be explained. For a read-only tool with one well-documented parameter, the description is adequate but doesn't mention any dependencies, error conditions, or when to prefer this over siblings. It's minimally 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?

With a single parameter and 100% schema coverage, the schema fully documents the 'id' parameter. The description adds no further parameter meaning, so 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 provides a specific inventory of what the tool returns (current offers, lowest price, stats, reviews, specs), making the tool's purpose clear. However, it doesn't distinguish itself from siblings like get_price_history or check_product_url, so sibling differentiation is absent.

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 guidance on when to use this tool versus alternatives. The description lacks exclusions or routing to siblings, leaving usage 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 this a safe, read-only, closed-world operation, so the safety profile needs no restating. The description adds that names are in Brazilian Portuguese, a genuinely useful locale detail, but says nothing about ordering, size, or caching behavior.

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 tightly-written sentence with no filler, and the key point (what a category looks like) is front-loaded. It is slightly elliptical in phrasing ('as name ... and slug'), which costs it the top mark.

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?

An output schema exists, so return values need not be re-explained, and the description covers the resource, its fields, and its downstream use with search_deals. For a zero-parameter lookup tool this is close to sufficient, with only ordering/deduplication details absent.

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 tool takes no parameters, so there is no parameter documentation burden to carry. That meets the baseline for a zero-parameter tool, and the description correctly spends its words on the return shape instead.

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 the resource (Pechincha.ai categories) and the exact fields returned (name in Brazilian Portuguese and slug), which is specific. It stops short of a clean verb+scope statement, but the connection to the search_deals category filter distinguishes it from siblings like list_stores.

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 phrase 'for the category filter of search_deals' implies this is a lookup helper to call before filtering deals, which is useful routing. However, it never explicitly says when to call it or what to do with the result, leaving the workflow 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 readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds two real behavioral facts beyond that: only *active* coupons are returned, and the content is in Brazilian Portuguese, which is useful context for result interpretation.

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 short, front-loaded sentences with no filler. It is terse to the point of omitting any usage context, but nothing is 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?

For a single-parameter read tool with an output schema and full annotation coverage, the essentials are present: what is returned, the active-only filter, and the content language. Usage routing and result format are the only gaps, and the latter is covered by the output schema.

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 single 'store' parameter is fully documented in the schema, including the slug format and a pointer to list_stores. The description adds no parameter detail beyond that, 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+resource: active coupon codes belonging to a store. It clearly identifies the entity being listed, though it does not explicitly contrast itself with siblings like get_deal or search_deals.

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?

There is no when-to-use guidance, no mention of prerequisites, and no routing to alternatives. The only hint is the implicit 'of a store' scoping, which is left for the agent to infer.

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, destructiveHint, and openWorldHint, so safety is covered. The description adds genuinely useful context beyond that: the 1000+ store count and the note that its output feeds search_deals and list_store_coupons.

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 compact sentences, front-loaded with what the tool returns and followed by the filtering constraint. 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?

An output schema exists, so return-format details are unnecessary, and annotations cover safety. The description adds scale context and downstream consumers, completing what the agent needs.

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 `query` parameter is already fully documented with an example. The description repeats the filter-by-name hint but adds no syntax or behavior 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?

States a specific verb (list) and resource (stores), and names the two sibling tools that consume its output (search_deals, list_store_coupons). An agent can tell what this returns and why it exists.

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?

Says to pass `query` to filter by name and warns that there are 1000+ stores, which gives clear context for when the filter matters. It does not explicitly say when NOT to use this versus list_categories, but the usage 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds real behavior beyond that: expired deals are excluded, results are newest-first at 25 per page, prices are in BRL with old price and discount %, content is Brazilian Portuguese, and query is a literal substring match rather than semantic. Minor gaps (total result counts, what happens when nothing matches) remain.

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 scope, ordering, and page size are front-loaded, followed by result contents, language constraint, matching caveat, and price usage. Almost every sentence carries actionable content, though the single dense paragraph packs several distinct topics (result shape, language, matching behavior, price params) that would scan better as separate sentences.

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?

An output schema exists, so result-field enumeration is arguably redundant, but the description still covers what an agent needs: freshness filtering, ordering, page size, currency, language of the corpus, substring matching semantics, and price filtering. The only real omissions are explicit behavior for zero results and relationship to sibling lookup tools.

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 83% (baseline 3), but the description adds genuine semantics beyond the schema: `query` is a literal substring of title or description, and min/max price map to named budget scenarios ("até R$ 3.000", "entre R$ 100 e R$ 200"). It also implies store/category slugs come from list_stores and list_categories, reinforcing the schema descriptions.

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 ("Search active (not expired) deals on Pechincha.ai") plus scope details: newest-first ordering, 25 per page, and the fields each result carries. This clearly distinguishes it from get_deal, get_product, and list_stores without needing to open their schemas.

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 concrete guidance: search in Portuguese because content is pt-BR, prefer short terms, and use max_price for budgets and min_price for floors/ranges. It also warns that accessory matches pollute results ("notebook" returns backpacks) and that titles should be checked. It stops short of naming explicit alternative tools for narrower lookups (e.g., get_deal for a known deal), so it's strong but not fully routing-aware.

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