VisionLibra

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

Annotations already establish readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds useful content scope (it discloses that pricing, availability, lead time and warranty terms are included), but says nothing about what happens on an unknown product name or whether editions are returned as a list.

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 zero preamble and no repeated information. The long comma-separated field list is dense but each item earns its place by telling the caller what the response contains.

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 field enumeration is the main way an agent learns the return shape, and it covers hardware, commerce and policy data. Error behavior for missing products and the edition/price structure are the remaining gaps.

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 already gives id/name examples, so the description carries no burden here. It adds no resolution rules (e.g. whether partial or case-insensitive names are accepted), 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+resource ('full specification sheet for one product') and enumerates the domains covered, which makes the scope unmistakable. It doesn't explicitly position itself against siblings like search_products or request_quote, so it falls 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 phrase 'for one product' implies this is a follow-up lookup once a product is known, but there is no explicit when-to-use or when-not-to-use guidance and no mention of alternatives. Contrast with request_quote, which an agent might otherwise pick for pricing.

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 mark this as a non-read-only, open-world, non-idempotent call; the description usefully adds the outbound channel and latency (sales replies by email, usually within one business day), which the annotations cannot convey. It does not warn that repeat submissions would generate duplicate inquiries despite idempotentHint=false, nor describe what the caller sees on success — a modest gap for a write tool with 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?

Two tight sentences with the purpose first and the gating condition second; nothing is redundant. The parenthetical enumeration of inquiry types is slightly dense but earns its place by scoping what qualifies as a quote request.

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 10-parameter write tool with no output schema, the description covers the essential gating rule, destination and turnaround but leaves the agent unsure how optional fields (budget, timeline, country) should be gathered and what confirmation or reference the user receives. Adequate to avoid misuse, not complete enough to drive a fully informed collection flow.

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 only 40% across 10 parameters, so the description should carry more weight. It does add real meaning by constraining name, email and organization to the user's own details, but it says nothing about budget, country, timeline, or the products quantity field, leaving over half the surface undocumented in either place.

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 and resource — submit an inquiry (hardware, volume pricing, dataset licenses, data services) to the VisionLibra sales team — so the agent knows exactly what the tool does. It doesn't explicitly name the read-only siblings (search_products, search_datasets, get_product_specs) to contrast against, but the submission purpose is unmistakably distinct from 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 states an explicit precondition set: only call when the user has asked for a quote AND supplied their own name, email and organization. That is both a when-to-use rule and an implicit when-not-to-use rule, which is exactly what an agent needs to avoid premature or hallucinated invocations.

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 the safety profile is covered. The description usefully adds that results include both datasets and purchasable data services with per-hour pricing, but says nothing about result volume, paging, or how keyword matching behaves.

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, but the tail becomes a dense services catalogue (collection, annotation incl. RGB-D/3D, trajectory labeling, policy evaluation, per-hour pricing) that reads as marketing rather than operational guidance.

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 optional params and read-only annotations, the call mechanics are simple, but for a search tool the description gives no sense of what a result looks like (dataset entries vs service listings, whether pricing accompanies each hit) and leaves the domain dimension unexplained.

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%: 'query' is documented in the schema while 'domain' is an undocumented 12-value enum. The description gestures at domain-like categories (warehouse, annotation, kitchen) but does not explain how they map to the enum or how query and domain combine.

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 (search) and a specific resource (VisionLibra's off-the-shelf egocentric task datasets plus associated data services), and enumerates the service categories covered. It is distinguishable from search_products by the dataset/service scope, though it never names the sibling to contrast with.

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 statement of when to pick this over search_products or get_product_specs, and no prerequisites or exclusions are given. The only usage hint ('Empty lists everything') lives in the schema, not the description.

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 the safety profile needs no restating. The description adds genuine behavioral value by disclosing the response payload (price, availability, lead time, key specs), which the agent cannot infer elsewhere since no output schema exists. It says nothing about result limits, pagination, ranking, or whether zero-result queries fail or return 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 sentences, both load-bearing: the first scopes the catalog with concrete model names, the second declares the return payload. No filler, no restatement of the tool name, and the most decision-relevant information (what is searchable) is front-loaded.

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 zero-required-parameter read-only search over a closed catalog, this is close to complete: what is searchable and what is returned are both stated, and annotations cover safety. The remaining gap is operational — no mention of result-count caps, ordering, or how to widen an over-narrow query — which matters when the agent iterates toward a quote.

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 67%, so the description only needs to close a modest gap. Its product-family enumeration usefully maps colloquial families onto the undocumented 'category' enum values (e.g. Wego2/4/6 -> 'Egocentric capture rig', Spatial Robot -> 'LiDAR'), which is real added meaning. However it says nothing about 'max_price_usd' semantics or how 'query' keywords relate to category filtering, leaving the conjunction behavior unstated.

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 opens with a precise verb+resource ('Search VisionLibra hardware') and then enumerates the exact product families covered (Wego rigs, ToF sensors/cameras, Spatial Robot LiDAR, Spatial Hub/RobooPi computers, companion robots), so the agent knows exactly what corpus is searched. It also states what comes back (price, availability, lead time, key specs), which implicitly separates it from get_product_specs. It never names a sibling outright, so this 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?

Usage is only implied: the enumerated catalog makes it obvious this is the entry point for finding VisionLibra products, and the stated return fields hint that this is a browsing/pricing tool rather than a deep-spec or quote tool. There is no explicit 'use this before request_quote' or 'use get_product_specs instead when you already have a product ID' routing, and no guidance on iterative refinement.

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