Pouncio: Vinted resale prices

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 description's job is to add context — and it does: 'Pouncio judges from its own data and does not open the page, so pass the asking price if the person mentions it' explains the non-fetching behavior and its consequence. It also discloses which data sources the verdict draws on (resale model price, seller record when known). It stops short of describing the shape of the verdict itself.

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 doing distinct work: trigger, internal behavior/rationale, exclusion with fallback. The usage condition is front-loaded and there is no filler or restatement of the tool name.

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 conveys enough: it states what is compared (price vs. resale model, seller record) and that judgment comes from internal data, so the agent knows what kind of verdict to expect. Only the concrete verdict structure is left implicit, which is a minor gap for a read-only lookup tool.

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 baseline is 3; the description earns a bump by explaining why asking_price matters — because the tool does not open the page, the price must be passed explicitly if the user mentioned it. That is a semantic rationale the schema field text ('The listing's price, if the person said it') does not convey on its own.

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 a specific verb and resource — evaluating a shared Vinted listing link for deal quality, resale value, or scam risk — and the scope is immediately distinguishable from siblings like check_resale_price and check_listing_text. An agent knows exactly what question this tool answers.

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 gives an explicit trigger ('when someone shares a Vinted listing link and asks whether it's a good deal, worth buying to resell, overpriced, or a scam') plus an explicit exclusion and the alternative to use instead ('Do not use without a Vinted link (use check_resale_price)'). Both use and non-use conditions are stated, not inferred.

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, idempotent, non-destructive, closed-world), yet the description still adds real value: the taxonomy of checks it performs (off-platform payment, refusing Vinted verification, iCloud/Google lock, named fault, box only) and its provenance ('the same text checks Pouncio's buying bots run before paying'). It stops short of noting limits such as false positives or that matched text is surfaced only within the quoted words, so not a 5.

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 usage trigger, then exclusions, then return semantics — a sensible order. The parenthetical examples are illustrative and earn their place, though the single dense paragraph could be broken up for faster scanning.

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?

There is no output schema, and the description compensates by stating exactly what comes back ('each warning sign in the quoted words'), plus language coverage and scope boundaries. Nothing an agent needs in order to call this correctly for a one-parameter read-only tool 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 coverage is 100% and the parameter is already described, so the baseline is 3; the description earns above that by constraining the input to quoted/pasted text and declaring seven supported languages, which the schema does not say. It still adds no guidance on malformed input or the 4000-character cap 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?

States a specific verb and resource ('check ... listing for red flags') and names exactly what input it consumes (a quoted seller/buyer message or listing description). It explicitly separates itself from check_resale_price and check_listing_deal, so an agent can route without opening a 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?

Gives concrete trigger examples ('A Vinted seller wrote: "pay me on WhatsApp..."'), then explicit exclusions: no quoted text, prices go to check_resale_price, bare links go to check_listing_deal. Both when-to-use and when-not-to-use are stated with the named alternative.

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 read-only, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuine behavioral context beyond that: which product categories the estimate is reliable for (electronics, consoles, phones, laptops, branded items named by model), which is a support boundary an agent needs. It does not say how the price is derived or what the response contains, but the annotation set lowers the bar.

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, front-loaded with the trigger condition, followed by scope limits and then exclusions with named alternatives. No filler and every sentence carries routing or scoping information.

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 only two parameters, full schema coverage and rich annotations, the main remaining gap is that there is no output schema and the description never indicates what comes back (a point estimate, a range, per-site pricing). The domain guidance compensates largely but not entirely for that absence.

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 baseline is 3. The description still adds real meaning for the `product` parameter by requiring model-level specificity ('branded items named by model', example 'PS5 Slim') and clarifying that vague or generic items are out of scope, which the schema's 'as specific as the person said it' only half states.

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 concrete verb and resource ('what a second-hand item sells or resells for on Vinted'), plus a secondary purpose (max buy price to resell at a profit), with a concrete example query. It explicitly names the sibling tools it is not and the situations they cover, so an agent can route without opening any 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?

Gives positive triggers (what a used item sells for, what it's worth used, max price to pay for resale profit), an explicit 'Do not use for' list (new retail prices, one specific listing link, what to list one's own item at), and names the correct alternative tools (check_listing_deal, suggest_listing_price) for two of those cases.

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 establish this as a safe, read-only, idempotent lookup. The description adds real behavioral context beyond that: the ranking is based on the gap between asking prices and sold prices after buyer protection and shipping, weighted by sales volume, and limited to common electronics categories. It stops short of describing result count, freshness, or output shape, but is substantially informative.

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?

It is front-loaded with the triggering situations, then explains the ranking logic and scope, then closes with exclusions. Every sentence earns its place and there is no wasted preamble.

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 three-parameter, read-only discovery tool with no output schema, the description covers purpose, usage boundaries, ranking methodology, and category scope well. It could more explicitly describe the shape of ranked results, but nothing critical 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 all three parameters are already documented in detail. The description's examples loosely illustrate budget and category usage but add little semantic detail 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 and resource — ranking models worth flipping — and directly names the sibling tools it is not (check_resale_price, check_listing_deal). An agent can distinguish this tool's role without opening any 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?

It gives explicit trigger examples ('what can I flip with 200 euros?', 'which iPhones are worth reselling?') and explicit exclusions with alternative tools for single-item pricing and listing-link checks. The routing decision is fully specified.

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 (read-only, idempotent, non-destructive, closed-world), so the description's job is to add context. It does so usefully by naming the three return components (model price, quick-sale price, current asking prices), which matters since there is no output schema. It stops short of any failure behavior, e.g. what happens for an unrecognized item or an unsupported site.

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 tight sentences: usage trigger and scope first, then outputs, then exclusions. Every sentence carries distinct information and there is 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?

With no output schema, the description correctly compensates by summarizing the returned price set, and the schema fully documents all three parameters. Only marginal gaps remain, such as edge-case behavior for unknown items or regions and whether condition materially shifts the estimate.

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% with per-parameter descriptions including the allowed site codes and currency implication, so the schema carries parameter meaning. The description adds no parameter-level detail beyond it, which is the expected baseline when the schema is complete.

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 concrete verb and resource (price a second-hand item to list on Vinted) and describes the outputs it returns. It also explicitly separates itself from check_resale_price (buying) and check_listing_text (writing), so siblings are clearly delimited.

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 explicit triggers (seller asks what price to list at, or how fast it will sell at a price) and explicit exclusions with named alternatives for both the buying and listing-text cases. Nothing 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.