Gradient Dynamics: thermal and CFD simulation

List PhysicsAI models

list_models

Read-onlyIdempotent

List immutable PhysicsAI releases, supported geometries and whether each release is enabled. Disabled models cannot be invoked; never substitute a different model silently.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_models"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 13, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered; the description adds real value beyond that by stating releases are immutable, that disabled models cannot be invoked, and that silent substitution is forbidden. It does not mention caching or freshness of the listing, a minor gap.

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?

Two short sentences with zero filler. The listing scope is front-loaded and the operational constraint follows immediately.

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?

An output schema exists, so return-value explanation is not required, yet the description still summarizes the payload (releases, geometries, enabled flags). For a zero-parameter read tool this is complete enough to call correctly.

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 takes zero parameters (schema coverage 100%), so there is no parameter semantics to explain; baseline 4 applies. Nothing in the description invents or misdescribes inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb + resource ('List immutable PhysicsAI releases') and states what each entry includes (supported geometries, enabled status). It is easily distinguished from sibling list tools such as list_jobs or list_projects by the resource named, though it does not explicitly contrast against them.

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?

Usage is implied rather than stated: 'Disabled models cannot be invoked' hints this is a pre-flight check before predict_physics, but the description never says when to call it or names an alternative. The 'never substitute a different model silently' line is a downstream-use rule, not selection 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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