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Gradient Dynamics: thermal and CFD simulation

Check prediction request

validate_prediction
Read-onlyIdempotent

Validate an inference request against an enabled immutable model release, without running it. Model availability is configured separately from MCP.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model_id": "<model_id>",
      "model_version": "<model_version>",
      "geometry_artifact_id": "<geometry_artifact_id>"
    },
    "name": "validate_prediction"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_idYes
conditionsNo
project_idNo
model_versionYes
include_fieldsNo
geometry_artifact_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the bar is lower, and the description still adds real behavior: nothing is executed, the target release is immutable and must be enabled, and availability is managed outside MCP (so failures may be environmental, not schema errors). It stops short of saying what the validation actually checks or how failures surface, but that is partly covered by the output schema.

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, front-loaded with the action and scope, with the second sentence carrying a non-obvious operational constraint. Nothing is redundant with the title or annotations.

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

Completeness3/5

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

For a read-only validator with an output schema and full annotation coverage, the description covers the essentials of what it does and the no-execution guarantee. It is still thin on the parameters and on what a successful versus failed validation means, which matters for a six-parameter tool with no schema descriptions.

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

Parameters2/5

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

Schema description coverage is 0% and the description mentions no parameters at all. Six inputs including model_id, model_version, geometry_artifact_id, conditions, project_id and include_fields are left entirely to the schema, and the description adds no meaning such as which version/release must match or what include_fields toggles.

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?

States a specific verb and resource: 'Validate an inference request against an enabled immutable model release', plus the key qualifier 'without running it' which makes it a dry-run rather than an execution. It is distinguishable from validate_geometry/validate_mesh/validate_simulation by naming the inference request as the object, though it never names a sibling explicitly.

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?

'Without running it' implies the pre-flight use case, and 'Model availability is configured separately from MCP' hints that availability problems are out of scope here. There is no explicit statement of when to prefer this over just submitting the prediction, no prerequisites, and no exclusions.

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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