御府和田玉 在售作品查询

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

No annotations and no output schema, so the description bears the whole burden and adds only the max-6 limit. It says nothing about what the comparison returns (side-by-side table? merged diff?), the ordering of results, or what happens with invalid IDs, fewer than two items, or duplicate entries — all decision-relevant for a compare tool.

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 short clause that front-loads the action, the resource, and the limit in that order, with zero filler. It is tightly written; its brevity is a deliberate trade-off rather than padding, though it leaves the reader wanting more.

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 annotations, no output schema, and no parameter documentation, this is a bare-bones definition for a tool whose entire value is the shape of the comparison it produces. The agent knows the input bound but not the return semantics or failure modes, which is thin for a dedicated compare operation.

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 0% — the array of strings has no description and no maxItems — so the description must carry the parameter alone, and it does: it declares the 6-item ceiling and identifies each item as an official website ID. That is meaningful constraint and format information absent from the schema, though it does not describe error behavior for oversized or malformed lists.

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 (compare) with a specific resource (works/products) plus a hard scope limit of 6 items, so it is clearly distinguishable from get_product and search_products. The phrase '官网编号' also pins the identifier domain. However, it never explicitly names the sibling alternative, so it stops short of a 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?

The 'up to 6' cap implies this is a bulk/multi-item operation rather than a single lookup, which lets an agent infer it should be chosen over get_product when several items must be viewed together. There is no explicit 'use this when / don't use this when', no stated minimum, and no mention of when a single lookup or search would be better.

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?

No annotations exist, so the description carries the full burden. It does add one genuinely useful behavioral fact — that awards and auction records come with sources (附出处), signalling authoritative, citable output — but says nothing about coverage limits, whether data is cached/current, or what a call returns when topic is empty.

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 the resource scope and finished with the parameter note. Nothing is redundant, though the scope list (brand / stores / creator) is dense enough that the topic sentence is slightly cryptic on first read.

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 no output schema, the description covers subject matter and sourcing but omits the default-topic behavior and gives no hint of the shape of the returned facts. Adequate, with clear gaps an agent would notice at call time.

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 0%, so the description must compensate, and it only partly does: it explains that topic accepts awards or auctions, adding meaning beyond the bare enum. The third enum value (empty string) is never explained, leaving the default/all behavior ambiguous.

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 resource set: the brand, its stores, and creator 付雪飞, plus the kind of material returned (verifiable records with cited sources for awards and auction results). This clearly separates it from the product-oriented siblings (compare_products, get_product, search_products), though it never explicitly says so.

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: an agent can infer this is the tool for brand/creator background rather than product lookup, and the topic options hint at two query modes. There is no explicit when-to-use vs. when-not, no mention of prerequisites, and no statement about what happens when topic is omitted.

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?

No annotations exist, so the description carries the full burden; it discloses that the result includes both 详细资料 (details) and 购买方式 (purchase method), and 查询 implies a non-mutating read. However, it never states behavior when the ID is unknown (empty result vs. error), whether data is live or cached, or any auth requirement, leaving meaningful gaps for a no-annotation tool.

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 with no filler, and the retrieval scope is stated before the returned content. It is appropriately sized for the task, though it is short enough that it leaves the usage and error questions unanswered.

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-required-parameter lookup with no output schema and no annotations, the description covers purpose, ID meaning, and what comes back, which is close to sufficient. The main missing piece is fallback behavior when the product number is not found.

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 0% and the parameter is a bare 'id' string, so the description must compensate. It does clarify that the value is the official-website product number rather than an internal ID or SKU, which is genuine added meaning, but it gives no format, example, or validation hint.

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 uses a specific verb (查询, query) and resource (商品/作品 details plus 购买方式, purchase method) and scopes retrieval to the official-site product number, which distinguishes it from search_products and compare_products. It does not explicitly name a sibling to contrast with, which keeps it from a 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?

Saying the lookup is 'by official website product ID' implies you must already have an ID, hinting that search_products comes first, but the description never states when to use this versus search_products or get_brand_facts, and gives no exclusions or prerequisites.

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?

With no annotations present, the description carries the full disclosure burden. It does usefully reveal that results are real-time, one-price-per-item, and include official links, prices, weights and award provenance, but it says nothing about result caps (the schema's limit max 30), empty-result behavior, or ordering defaults.

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 tight paragraph that front-loads the resource and scope, then parenthesizes the returned fields and closes with two concrete examples. Nothing is redundant, though the example clause could carry parameter-mapping value more explicitly.

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 six-parameter search tool with no output schema and no annotations, the description covers the important ground: what is searched, that data is live, and what each result contains. The remaining gap is query-behavior detail (sort semantics, result limits), which keeps it short of full completeness.

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 low (17%), so the description needs to compensate. It adds real meaning to `query` (free-form natural language) and the examples implicitly map onto min_price/max_price ('around 200k') and award_only ('award-winning'), but `sort` and `limit` are never explained.

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: querying currently-on-sale Hetian jade carvings from the Yufu official site, with scope (in-stock, real-time, one item per price). It does not name or distinguish itself from the siblings (get_product, compare_products, get_brand_facts), 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?

Usage is only implied through example queries ('a 200k seed-material bracelet', 'award-winning Guanyin pendant'), which convey the natural-language style of input. There is no explicit statement of when to prefer this over get_product for a single item or compare_products for comparison, and no exclusions.

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