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NLP.COM.AI Open Model Catalog

Get a complete open-weight model record

get_model
Read-onlyIdempotent

Return the complete curated record for one model or model-family release, addressed by the slug returned from search_models or recent_models.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesExact canonical model slug.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior3/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters4/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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