NLP.COM.AI Open Model Catalog

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered by structured data. The description adds only 'complete curated record', which signals full-record vs. partial results, but says nothing about unknown-slug behavior, error modes, or rate limits.

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 sentence with no filler; the resource and its identifying key are front-loaded and the slug provenance trails as supporting detail.

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 an output schema present the description need not document return values, and annotations cover the safety profile, so the core call is fully specified. The only remaining gap is what happens on an unrecognized slug and how model vs. model-family releases differ in the response.

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 defines slug as the 'Exact canonical model slug', so the baseline is 3. The description goes beyond it by telling the agent where valid slugs come from (search_models / recent_models), which is genuinely useful provenance not present in 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 ('Return') and resource ('the complete curated record for one model or model-family release'), and distinguishes itself from the list-style siblings by positioning itself as the single-record fetch keyed by a slug.

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 makes the workflow explicit by naming search_models and recent_models as the sources of the slug, which tells the agent when this tool is the right next step. It stops short of stating when NOT to use it (e.g., to browse or compare multiple models), so it is clear context rather than 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 declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds only the notion of a 'curated' (closed) set and recency ordering; it says nothing about pagination, result ordering guarantees, or how many records the curated set contains.

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?

One sentence, front-loaded with the action and resource, with the optional filter stated last. No filler or redundancy.

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?

An output schema exists so return values need not be explained, and annotations carry the safety profile. The gaps are the unmentioned 'limit' parameter and the absence of any sibling differentiation, which matters given search_models and get_model are close alternatives.

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%: 'since' is documented in the schema and reinforced in the description ('on or after', i.e. inclusive), but 'limit' is never mentioned in the description and relies entirely on the schema's default/min/max. Baseline 3 is appropriate given partial schema coverage.

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 ('Return') and resource ('newest curated open-weight model release records') with a clear recency scope. It does not, however, distinguish itself from the sibling search_models or get_model, so the agent must infer the boundary.

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 by the word 'newest' and the optional date filter; there is no explicit when-to-use, when-not-to-use, or pointer to search_models for broader queries. An agent can guess the intent but gets no 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?

Annotations already declare readOnly, idempotent, non-destructive and closed-world (openWorldHint=false), so the safety profile needs no restating. The description adds that results are canonical NLP.COM.AI pages plus primary sources, which is useful provenance context, but it says nothing about result counts, ranking, or that limit caps at 20.

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 verb and resource front-loaded and no filler. The second sentence about return sources earns its place by signaling provenance, though it is the least essential part.

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?

An output schema exists, so return-shape explanation is unnecessary, and annotations carry the safety/behavior profile. What remains missing is when to prefer this over its two siblings and any note on the limit parameter's range, which keeps this short of 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?

Schema coverage is only 50%: the query parameter is documented as an AND-match ('words that every matching record must contain'), while limit has no description at all. The description's facet list enriches query semantics slightly but does not compensate for the undocumented limit or explain the 1-20 cap.

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 (search) and resource (curated open-weight model release records) and enumerates the searchable facets: model name, developer, license, parameter/context description, technical features. It is clear what the tool does, though it never names or contrasts the siblings get_model and recent_models, so an agent must infer the boundary.

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 when-to-use guidance, no exclusions, and no mention of the sibling tools. The agent is not told when to search versus fetch a single model (get_model) or browse the recency-sorted list (recent_models), which is the main routing decision for this tool.

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