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check_silent_failures

Identify silent-failure risks in Abaqus models across mesh, constraints, contacts, elements, job output, and rigid-body motion, and get a pass/fail report.

Instructions

Run silent-failure checks on the current Abaqus model.

Detects 7 categories of model issues that Abaqus does not report as errors:

  1. Mesh integrity: parts with zero elements, unmeshable hex requests

  2. Constraint coverage: tie constraints that may silently drop nodes

  3. Volume/logic: cut operations that removed nothing, degenerate geometry

  4. Contact validity: contact pairs without adjacency

  5. Element quality: risky elements (C3D8R hourglass), hourglass-prone configs

  6. Job output: completed jobs with no ODB, meaningless exit codes

  7. Unconstrained parts: instances free to undergo rigid body motion

These checks measure the model you built, not just the answer it produced.

Args: model_name: Name of the model to check (default: first available model). workdir: Working directory for job output checks (default: current).

Returns: Structured Markdown report with pass/fail/warning findings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
timeoutNo
workdirNo
model_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden, and it largely succeeds: it details the seven categories examined and states the return format is a structured Markdown report with pass/fail/warning findings. It does not explicitly state whether the operation is read-only or whether it requires an active Abaqus connection, but 'check' and 'detects' imply a non-mutating diagnostic.

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 front-loaded with a clear one-line summary and then uses a well-organized numbered list to communicate the seven check categories. Every section earns its place: categories, a clarifying philosophy sentence, and parameter explanations. The length is justified by the complexity of the tool.

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 the high complexity of the tool, the description covers the core behavior, the model scope, the parameter defaults, and the output format. It is nearly complete, but it does not explain the timeout parameter's role or expected interaction with a live Abaqus session, which would make it fully self-sufficient.

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?

The schema has 0% description coverage, so the description must compensate for model_name, workdir, and timeout. It does explain model_name (default first available model) and workdir (working directory for job output checks), but it omits timeout entirely even though that parameter appears in the schema without documentation. This is partial compensation for low schema coverage.

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 opens with a specific verb and resource: 'Run silent-failure checks on the current Abaqus model,' and then enumerates 7 concrete check categories, making the purpose unambiguous. It does not explicitly differentiate itself from the similarly named sibling check_model_integrity, but the 'silent-failure' framing and focus on issues Abaqus does not report as errors provide enough conceptual distinction.

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 gives clear contextual guidance, especially 'These checks measure the model you built, not just the answer it produced,' which tells an agent to use this after model construction to catch hidden issues. It does not explicitly name alternative tools or state when not to use it, but the intended scenario is reasonably clear.

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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