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viznoir

by kimimgo
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Python
Hybrid

inspect_physics

Analyze simulation data to extract vortex detection, critical points, and solver metadata as structured JSON for AI storytelling.

Instructions

Extract structured physics data for AI storytelling.

Analyzes simulation data to extract:

  • L2 FieldTopology: vortex detection (Q-criterion), critical points, centerline profiles, gradient statistics per field

  • L3 CaseContext: boundary conditions, transport properties, solver info, mesh quality, derived quantities (Re, Ma, etc.)

Returns structured JSON for LLM to build physics narratives. Replaces analyze_data with quantitative topology data instead of hardcoded heuristics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYesPath to VTK/OpenFOAM/CGNS simulation file
case_dirNoOpenFOAM case directory for full solver metadata. If None, only mesh quality is extracted.
fieldsNoSpecific field names to analyze (None = all fields)
probe_linesNoNumber of auto centerline probe lines (1-3)
vortex_thresholdNoQ-criterion threshold for vortex detection

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changedv0.8.0
    • addedInput schema / properties / case_dir / description
      Added value: +"OpenFOAM case directory for full solver metadata.\n      If None, only mesh quality is extracted."
    • addedInput schema / properties / fields / description
      Added value: +"Specific field names to analyze (None = all fields)"
    • addedInput schema / properties / file_path / description
      Added value: +"Path to VTK/OpenFOAM/CGNS simulation file"
    • addedInput schema / properties / probe_lines / description
      Added value: +"Number of auto centerline probe lines (1-3)"
    • addedInput schema / properties / vortex_threshold / description
      Added value: +"Q-criterion threshold for vortex detection"
  2. First observedv0.7.0

TDQS

A3.9/5.0
Behavior4/5

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

No annotations provided, so the description carries full burden. It details extracted data categories (vortex detection, boundary conditions, etc.) and returns structured JSON, implying read-only behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is three paragraphs, front-loaded with main purpose, and efficient in listing capabilities without unnecessary words.

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?

Given high schema coverage and output schema present, the description adequately explains input/output and tool replacement context, though it could mention file format compatibility.

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 100%, but the description adds no extra parameter details beyond the schema. Baseline of 3 is appropriate.

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 states 'Extract structured physics data for AI storytelling' with specific details like L2/L3 structures. It distinguishes from sibling 'analyze_data' by replacing hardcoded heuristics with quantitative topology data.

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?

The description explicitly says it replaces 'analyze_data' and outlines extraction of specific physics data, giving a clear alternative context. However, it does not provide exhaustive when-not-to-use scenarios.

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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