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 adds genuinely non-obvious behavior beyond that: unknown slugs are collected into `missing` rather than aborting the whole call, which is a partial-failure semantic an agent cannot infer from the 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 sentences, zero filler, with the core purpose front-loaded and the edge-case behavior second. Nothing could be removed without losing 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 no output schema, the description carries the burden of describing the response, and it only explains the `missing` field. It never says what a 'side by side' record contains or how the 2-5 results are keyed/ordered, leaving the main return shape undocumented.

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 `slugs` parameter already documents its 2-5 range and origin in search_manufacturers, so the description only echoes that bound. The one added detail — that unresolvable slugs surface in `missing` instead of erroring — is useful but belongs to output behavior more than parameter meaning.

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?

Specific verb (compare, framed as 'return 2-5 records side by side') and resource (manufacturers). The 2-5 cardinality distinguishes it implicitly from the single-record sibling get_manufacturer, though no sibling is named.

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?

'for a sourcing decision' supplies a clear triggering context, and the 2-5 range scopes when the tool applies. It stops short of naming an alternative (e.g. call get_manufacturer for a single record) or stating exclusions.

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 (readOnlyHint, openWorldHint=false), so the description's job is added context. It does that with a genuine scope disclaimer — 'PlushSource does not audit or certify manufacturers' — which tells the agent how to frame the data and avoid implying endorsement. That is behavior beyond what the annotations convey.

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 short sentences: return contents first, then the one actionable conditional, then the disclaimer. Front-loaded and free of filler; the disclaimer slightly trails but still earns 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?

There is no output schema, so the description appropriately enumerates the return fields and adds a caveat about data provenance. For a zero-argument metadata tool this is close to complete; only the relationship to sibling lookup tools is left implicit.

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?

Zero input parameters, so the schema imposes nothing and the baseline applies; no parameter discussion is needed or missing.

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?

Names a specific verb (return) and enumerates exactly what comes back: snapshot date, record count, licence, and links to PlushSource, the open dataset, and listing criteria. That is concrete enough to distinguish it from the manufacturer-centric siblings, though it never explicitly says it is the dataset-level metadata tool in contrast to 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?

Gives one concrete conditional: 'Use the criteria link when you need to explain what gets listed.' One use case is spelled out, but there is no guidance on when to reach for this versus search_manufacturers, get_facets, or compare_manufacturers, nor any prerequisite or exclusion.

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, closed world), and the description adds real behavioral value: only values with at least one manufacturer are returned, and counts are included. It stops short of describing the response structure (e.g., how facets and counts are keyed), which matters because there is 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 sentences, zero filler. The facet enumeration comes first as the core of the purpose, and the actionable 'call this before filtering' instruction is placed last so it is read immediately before deciding to invoke.

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 parameterless read tool with annotations and no output schema, the description covers what is returned (facet values plus counts) and the important empty-value exclusion. A little more on result shape would close the remaining gap left by the absent 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?

The tool takes zero parameters, so there is nothing for the description to disambiguate. Baseline of 4 applies; no parameter guidance is needed or possible.

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 enumerates exactly which facet dimensions are returned — countries, categories, certifications, product types, design inputs, industries, MOQ bands. This is clearly distinct from search_manufacturers or get_manufacturer, which return entity data rather than facet enumerations.

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 an explicit sequencing instruction: 'Call this before filtering so you only use values that exist.' That is a concrete when-to-use cue. It does not name a when-not case or any alternative, which is a minor gap given no sibling offers facet enumeration.

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 safety and scope are covered and the description needn't repeat them. It adds genuine value beyond that by specifying the returned payload contents, including provenance fields ('source links', 'PlushSource listing URL'), which tells the agent the record is citation-bearing. It does not state what happens when the slug is unknown or stale, which is the remaining behavioral gap.

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, no filler, and the identity-plus-field-list is front-loaded before the usage directive. Every clause earns its place and nothing is restated from the annotations or schema.

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-parameter read-only fetch with no output schema, enumerating the returned fields is exactly the compensation needed, and it is supplied. The only omission is failure behavior for an unresolved slug, which is minor but non-trivial for a tool whose output the agent is told to quote from.

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?

Only one parameter exists and the schema already describes it at 100% coverage, including its provenance ('as returned by search_manufacturers'). The description adds no format, casing, or normalization detail for the slug, so the baseline 3 for schema-covered parameters 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 and resource ('Return the full record for one manufacturer') and then enumerates exactly which fields come back: capacity, founding year, location, source links, and the PlushSource listing URL. That field list distinguishes it cleanly from search_manufacturers (discovery) and compare_manufacturers (side-by-side), so an agent can route without opening the 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?

The second sentence gives an explicit trigger: 'Use this before quoting any fact about a specific listing,' and the slug parameter is tied back to search_manufacturers, implying the fetch → detail workflow. There is no stated when-not condition or named alternative for the case where multiple manufacturers are needed (compare_manufacturers would apply), so it stops short of full routing 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?

With readOnlyHint and openWorldHint already declared, the description adds useful behavioral detail beyond annotations: array filters are ANDed, MOQ filters exclude records with unknown MOQ, and country matching is limited to the primary country. It does not describe pagination or response fields in detail.

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 compact and front-loaded, starting with what the tool does and then giving the most important filtering constraints. Every sentence adds actionable information without repeating the schema.

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 13-parameter search tool with no output schema, the description supplies the key filter semantics and prerequisite guidance an agent needs to call it correctly. It does not fully explain return fields or pagination, but the core invocation context is covered.

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 at 38%, but the description compensates by explaining the most consequential semantics: AND behavior for array filters, MOQ exclusion behavior, and primary-country matching. It still leaves conventional parameters such as limit and offset without added meaning.

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: search manufacturers using free text and structured filters, returning a compact shortlist. It distinguishes the result shape from a detail or comparison tool, but does not explicitly name the sibling tools it differs from.

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 clear usage context: call get_facets first when unsure which countries, categories, or certifications exist, and it explains important filter behavior. It lacks explicit guidance on when to choose compare_manufacturers or get_manufacturer instead.

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