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Glama

List Models

list_models
Read-onlyIdempotent

List available image and video models with their credit cost.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already communicate readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds one useful behavioral detail beyond annotations: that each model has an associated credit cost, which informs the agent about the nature of the returned data. However, it does not mention response format, pagination, or ordering, but given the simplicity and annotation coverage, a 3 is appropriate.

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 a single sentence that front-loads the action and states the essential output detail. There is no wasted wording, and it is appropriately sized for a tool with no parameters and a simple return set.

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?

For a straightforward list tool with no parameters, no output schema, and annotations covering safety, the description provides sufficient context: it specifies what is listed (models) and a key attribute (credit cost). It could be slightly more explicit about the return structure (e.g., array of objects with model ID and cost), but that is a minor gap given the simplicity.

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?

The tool has zero parameters, and schema description coverage is 100% (vacuously). With 0 params, the baseline is 4, and the description does not need to explain any input semantics. It correctly focuses on the output (models and credit costs) rather than parameters, which is appropriate.

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?

The description clearly states the action (list), the resource (available image and video models), and a specific detail (credit cost). It distinguishes itself from siblings like generate_image and generate_video, which perform generation, and get_credits, which presumably queries credit balance. The verb and resource are unambiguous.

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

Usage Guidelines3/5

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

The description does not explicitly state when to use this tool versus alternatives. However, the purpose is clear and siblings are semantically distinct (generation vs. listing), so the intended usage is implied. No exclusions or conditions are provided, which keeps it at a baseline 'implied' level rather than explicit.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool maps cleanly to a distinct action: generate new media, edit media, retrieve by ID, list history, view models, check credits, and quote a potential cost. Even check_generation and list_generations are clearly separated by lookup-by-id versus listing. No two tools appear to do the same thing.

Naming Consistency5/5

Every tool follows the same verb_noun snake_case pattern, such as generate_image, list_models, and quote_generation. The verbs are descriptive and consistently chosen for each operation. This makes the tool surface highly predictable for an agent.

Tool Count5/5

Eight tools is well-scoped for a media generation server covering image generation, video generation, editing, history retrieval, model discovery, and credit management. Each tool serves a clear purpose without redundancy or feature bloat.

Completeness5/5

The tool set covers the full generation lifecycle: creating images, creating videos, editing images, checking generation status, listing past results, inspecting models and costs, quoting prices, and checking credits. There are no obvious dead ends or missing core operations for the stated domain.

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