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get_model

Read-onlyIdempotent

Fetch a model's input schema to see required parameters, defaults, and value ranges before building a request to run inference.

Instructions

Get one hosted model's input schema.

Backs ``GET /v1/models/{model_id}``. The ``input_schema`` is the
JSON Schema for ``run_model``'s ``inputs`` — property types,
defaults, enums, and min/max ranges — so read it before building a
request rather than guessing parameter names. Properties whose
``format`` is ``image_uri``/``video_uri``/``audio_uri`` take a
public HTTPS URL.

Also returns ``description``, ``categories``, ``base_price_usd``
per ``price_unit``, and ``model_url``.

Args:
    model_id: The model's identifier, slashes included, e.g.
        ``blackforestlabs/flux-1-kontext/pro/edit``. Find one with
        ``list_models``.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.1.0

TDQS

A5/5.0
Behavior5/5

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

Annotations already mark it read-only/idempotent/non-destructive; the description adds the input_schema structure, URL-format requirements for media properties, and the extra returned fields (description, categories, base_price_usd, model_url). It also clarifies slash-containing model IDs, matching realistic usage.

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?

The description is front-loaded with the core purpose and endpoint, then gives targeted return/usage details. Each sentence adds functional value; the Args section is compact.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an idempotent GET with one parameter, the description covers what is returned, how to construct the argument, and why to call this before run_model. The lack of an output schema is mitigated by listing the returned fields.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description fully compensates: model_id is explained with slashes included, a concrete example, and how to discover valid IDs via list_models. No parameter meaning is left to guesswork.

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+resource ('Get one hosted model's input schema') and clearly separates it from list_models (listing) and run_model (execution). The endpoint mapping and key returned field reinforce what the tool uniquely provides.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells the agent to read this before building a run_model request instead of guessing parameter names, and directs it to list_models for finding model_id. This gives both a when-to-use rule and a sibling alternative.

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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