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AstroQuestStudio

catia-v5-mcp

catia_batch

Run many CATIA tool calls in one round trip, validating every step against schemas before execution and stopping at the first failure.

Instructions

Run MANY catia_* tool calls in ONE round trip (10-100x fewer LLM turns). Every step is validated against the tool schemas BEFORE anything runs (unknown tool, misspelt/missing argument, wrong type, bad enum value): a typo in step 31 is reported up front and CATIA is left untouched. Steps then run in order and stop at the first failure (stop_on_error). Use dry_run=true to only validate. Compact per-step report with timings. Not nestable. Prefer it for any sequence you already know (sketch -> pad -> rename -> check), and keep separate calls for steps that depend on a value you must read first (e.g. face points from catia_list_faces).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stepsYesOrdered calls: [{"tool": "catia_create_sketch", "args": {"plane": "xy", "name": "Sketch_Base"}}, ...]
dry_runNoOnly validate every step; execute nothing (default false).
stop_on_errorNoStop at the first failing step (default true).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations only provide safety flags; the description adds substantial operational behavior: pre-execution validation against tool schemas, ordered execution, stop-on-error, dry-run, non-nestability, and a compact report with timings. It also clarifies failure behavior during validation ('CATIA is left untouched'). No contradiction with annotations.

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?

Front-loads the core purpose, then behavior, then usage in a tightly packed paragraph. Each sentence adds a distinct fact (validation, execution order, failure, dry-run, report, nesting, usage) with no filler.

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?

For a complex batch meta-tool, the description covers purpose, validation, execution flow, failure semantics, nesting constraint, report format, and usage routing. With full schema coverage and safety annotations already present, remaining omissions (e.g., maximum steps) are minor and do not impede correct invocation.

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 adds meaning by explaining that the steps array is validated for unknown tools, misspelt/missing arguments, wrong types, and bad enum values, and clarifies dry_run and stop_on_error as validate-only and halt modes. It does not expand on the args object structure beyond the schema example.

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: running many catia_* tool calls in one round trip. Explicitly distinguishes itself from sibling tools by telling the agent to prefer it for known sequences and keep separate calls when values must be read first. An agent can identify it without opening the schema.

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?

Gives explicit when-to-use ('Prefer it for any sequence you already know') and when-not ('keep separate calls for steps that depend on a value you must read first'), with a concrete example. Also names dry_run for validation-only usage. No inference needed.

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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