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Python
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FEM Thermal Results

fem_thermal_results
Read-only

Retrieve temperature field summaries from completed thermal simulations by providing an analysis or job ID. Returns min, max, mean temperatures and hottest nodes for quick thermal assessment.

Instructions

Extract temperature field summary from a completed thermal run. Pass analysis, or the job_id of a finished fem_run_submit solve. Returns {temperatures_c: {min, max, mean}, top_n_hot_nodes: [...]}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNo
job_idNo
analysisNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changedv0.5.5
    • addedInput schema / properties / analysis / anyOf
      Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
    • addedInput schema / properties / analysis / default
      Added value: +null
    • removedInput schema / properties / analysis / type
      Removed value: -"string"
    • addedInput schema / properties / job_id
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Job Id"
+}
    • removedInput schema / required
      Removed value: -[
-  "analysis"
-]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, and the description does not contradict that. It adds useful behavioral context by stating the operation extracts from a completed run and by specifying the return shape, including min/max/mean temperatures and top_n_hot_nodes. It does not discuss error behavior, but the annotation lowers the burden.

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 three concise sentences with no filler. The action is front-loaded, the usage guidance follows, and the return contract is stated directly.

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 no output schema, the description correctly provides the return structure. It covers the main selector parameters and the prerequisite of a completed thermal run. Minor gaps remain around `top_n` semantics and selector exclusivity, but overall it is sufficient for a read-only result query tool.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate. It explains that `analysis` and `job_id` are alternative selectors and implies `top_n` relates to the returned top_n_hot_nodes. However, it does not explicitly define `top_n`, its default, or what happens when both selectors are supplied or neither is supplied.

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 opens with a specific verb and resource: 'Extract temperature field summary from a completed thermal run.' This clearly differentiates it from sibling tools like fem_modal_results or render_fem_results by focusing on thermal temperature data extraction.

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 gives explicit context: use this on a completed thermal run by passing either `analysis` or the `job_id` of a finished fem_run_submit solve. It does not explicitly name alternative tools or exclusions, but the timing and selection criteria are clear enough for an agent.

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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