Dolphin Industrial Parts

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered by structured data. The description adds that the payload includes limits and a disclaimer, which is modest extra context, but it says nothing about response shape, size, or freshness.

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 sentences with no filler, and the content enumeration is front-loaded. The pricing sentence is tangential to the tool's mechanics but does carry a small routing signal, so it earns partial credit rather than being dead weight.

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 burden of describing returns, and the enumeration of included fields (site, contacts, endpoints, tools, limits, disclaimer) does that reasonably well for a zero-parameter read. It stops short of stating the format or when to call it, but an agent has enough to invoke it correctly.

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 zero parameters, so there is nothing for the description to disambiguate; the baseline for a parameterless tool applies. Schema coverage is reported at 100% with an empty property set, confirming no gap.

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 enumerates the manifest's contents (site, contact details, endpoints, tools, limits, disclaimer) so the resource is identifiable, but it never states a verb ('returns'/'retrieves') and reads as a content list rather than a purpose statement. It does not explicitly contrast itself with siblings like list_catalog or lookup_part, leaving the agent to infer that this is site-level metadata rather than catalog data.

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 explicit when-to-use guidance or statement of prerequisites. The trailing sentence 'Availability and price are quoted per application' only faintly signals that pricing is not obtained here (implying request_quote), but this is an indirect hint rather than a routing instruction.

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, idempotentHint, and closed-world, so safety is covered. The description adds real behavioral context beyond that: the listing includes model counts, and 'Availability and price are quoted per application' signals that pricing is not retrievable here and must go through a quote path instead.

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 sentences, front-loaded with what is listed before the caveat about pricing. Every clause carries information; nothing is padded.

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 tells the agent what comes back (brand names, equipment families, model counts) and clarifies that price/availability are out of scope. For a simple parameterless list tool this is essentially 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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a parameterless tool applies. No misleading parameter hints are present.

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 ('OEM brands and equipment families') and adds a scoping detail ('for which Dolphin Industrial Parts quotes compatible parts, with model counts'). This is clearly distinct from siblings like list_models or list_catalog, though it never names them 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 — an agent would call this to discover which brands exist before drilling into models or parts — but the description never states when to use it versus list_catalog or list_models, nor does it set any preconditions. Adequate minimum-viable guidance only.

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 readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds genuine behavioral context beyond that: fixed page size of 100, the return shape (part numbers and descriptions with paging info), and the explicit limitation that availability/price are not included.

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 with the core operation, page size, and return shape front-loaded. Every sentence carries distinct information (what it does, what it returns, what it deliberately does not return).

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 two-parameter read-only listing tool with no output schema, the description covers action, scope, page size, and return fields, which is close to sufficient. It could have stated the ordering of results or what happens past the last page, but nothing essential for 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 coverage is 100%, so the baseline is 3, but the description meaningfully extends the page parameter by disclosing the fixed 100-items-per-page window and confirms the single-family scope of the required family argument. That is real added meaning for pagination decisions beyond the schema's '1-based page number'.

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 (page through catalog parts) plus the scoping constraint (one equipment family) and page size (100 per page). It is clearly a bulk listing operation, distinct in spirit from lookup_part, but it never explicitly names or contrasts a sibling the way the strongest definitions do.

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: browse a family's catalog when you need part numbers/descriptions. The final sentence, 'Availability and price are quoted per application,' is a useful implicit routing hint that this tool is not the place to get pricing, but it never says so directly or names request_quote as the 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 readOnly, idempotent, non-destructive and closed-world behavior, so safety is covered. The description adds the useful caveat that availability and price are quoted per application, hinting those values are not part of this listing, but says nothing about result size, pagination, or completeness.

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: purpose first, prerequisite second, data caveat last. No repetition of the title and nothing that fails to earn 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 single-parameter read tool with full annotation coverage, the description gives the agent everything needed to call it correctly. The only minor gap is that no output schema exists and the description does not characterize what a returned model record contains.

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 the baseline is 3, but the description adds value by giving concrete valid brand values (IMO, Moatti, Alfa Laval) and pointing at list_brands as the source of the identifier, which the schema only states generically.

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 (equipment models and series) scoped to one OEM brand, with concrete brand examples. It is clearly distinguishable from siblings like list_brands and list_catalog.

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?

Explicitly routes the agent to list_brands for obtaining valid brand names, which is the key prerequisite. It does not clarify when to use this versus list_catalog or lookup_part, so it stops short of full when/when-not guidance.

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-open-world), and the description adds genuinely new behavior: a three-tier fallback that can return an unconfirmed guess, the fact that results are compatible alternatives rather than OEM parts, and that availability/price are quoted per application. These caveats materially affect how an agent should trust and present the result.

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, front-loaded with the action and match order before the return/caveat clauses. Dense and largely waste-free, though the second sentence stacks three distinct facts (outputs, non-OEM status, per-application pricing) that could be separated for scanability.

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 compensates by naming what comes back (matching parts, how to order, quote form URL) and the domain caveats. Only the no-match/zero-result case is left unspecified, a minor gap for a single-parameter 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 description coverage is 100% for the single part_number parameter, including length bounds and example formats, so the description adds no format or syntax meaning. Baseline 3 applies when the schema already carries the full parameter 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?

States a specific verb and resource ('Look up a part number') and goes further by spelling out the tiered resolution strategy (exact, then prefix, then format-based guess). This clearly separates it from siblings like list_catalog and get_manifest, which enumerate rather than resolve a single identifier.

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 by the verb (you call this when you already have a part number), and the description usefully warns that the third tier is 'not a confirmed match'. However, it never says when to prefer this over list_catalog/list_models or when to move on to request_quote, so the routing guidance 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.

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

The description goes well beyond the annotations: it explicitly states 'It never submits anything', describes what is returned (cleaned request, quote form URL, contact details), and discloses business constraints (USD currency, 1 business day turnaround, quote per application). The readOnlyHint/idempotentHint annotations are consistent with the 'never submits' claim.

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, front-loaded with the purpose, and the constraints follow logically. The parenthetical enumeration of fields is slightly redundant against a 100%-documented schema, but it costs little and aids 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?

With no output schema, the description takes on the burden of describing the return value and does so concretely (cleaned request, quote form URL, contact details). Combined with the 'never submits' guarantee and turnaround expectations, an agent has everything needed to call this correctly.

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% and all ten parameters are documented in the schema, so the schema carries the semantics. The description's parenthetical field list largely restates schema properties without adding format, constraint, or interaction detail, 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 ('Prepare') and resource ('parts quote request') and enumerates the fields involved, so the agent knows exactly what it produces. It doesn't explicitly contrast itself with the sibling lookup/list tools, but the name being a quote-prep tool rather than a catalog lookup makes the function unambiguous.

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: it tells you what a quote request contains and how quotes are handled (USD, 1 business day, priced per application), which implies when to call it, but there are no explicit when-to-use/when-not conditions and no sibling tool is named as an alternative.

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