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MIDAS CIVIL NX tools (MIDAS IT Europe)

Run the analysis

civil_run_analysis
Idempotent

Runs the CIVIL NX analysis of the open model (doc/ANAL), so results can be read with the result tools straight after building or changing a model. Replaces the model's results; changes no model data. The model must have been saved to a file at least once (the API cannot analyse an unsaved model): saveFirst: true saves it over its file first. Previews unless apply: true. Can take minutes on a large model.

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": "civil_run_analysis"
  }
}

Input Schema

NameRequiredDescriptionDefault
applyNoRun it. Only with the user's go-ahead.
saveFirstNoSave the model over its own file before analysing (doc/SAVE).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.9/5.0
Behavior5/5

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

Goes well beyond the annotations: it discloses that existing results are replaced while model data is untouched, that an unsaved model cannot be analysed, that saveFirst writes over the model's own file, and that a run can take minutes. These are exactly the side effects and operational constraints an agent needs before invoking, and they are consistent with readOnlyHint=false and destructiveHint=false.

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?

Five compact sentences, front-loaded with purpose, then side effects, precondition, preview gate, and cost. Nothing is padding and the ordering matches the decision order an agent would follow.

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?

No output schema exists, yet the description covers what the call produces (analysable results for the result tools), what it destroys (prior results), prerequisites, safety gate, and latency. Nothing material is missing for a two-parameter tool.

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?

Schema coverage is 100%, so the baseline is 3; the description still adds value by explaining that apply acts as a preview/execute switch rather than a plain flag, and that saveFirst is required because the API cannot analyse an unsaved model. Only minor redundancy with the schema's own text keeps it from a 5.

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 and resource (runs the CIVIL NX analysis of the open model) and names the API command (doc/ANAL). It also positions the tool in the workflow relative to siblings: results become readable via the result tools immediately afterward.

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 says when to use it (after building or changing a model, before reading results) and states the hard precondition that the model must have been saved at least once, with saveFirst as the remedy. It also documents the preview-vs-execute gate (apply: true) and the runtime caveat for large models.

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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