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Glama

nx_verify_result

Verify observed feature and body counts after a Siemens NX mutation, using a preflight ID to confirm the result matches expected values without claiming exact geometry.

Instructions

Verify observed feature/body state after a mutation; mock mode never claims exact geometry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
preflightIdYes
expectedBodyCountYes
expectedFeatureCountYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose the important behavioral trait that 'mock mode never claims exact geometry,' which alerts the agent to a limitation of the tool in simulation. However, it says nothing about whether the tool is read-only, requires specific permissions, or returns any status or error behavior, leaving significant gaps.

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?

The description is a single, front-loaded sentence that states the primary purpose before adding the mock-mode caveat. It contains no wasted words and is appropriately compact for a simple verification tool, though it could be more actionable with parameter or behavior details without becoming verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (three required params, no output schema, and no annotations), the description is too sparse. It does not explain what the tool returns (e.g., a boolean, a report, a state snapshot) or how the expected counts are validated, leaving an agent to guess at the invocation contract and result semantics.

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

Parameters2/5

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

The input schema has no descriptions for its three required parameters, and the description provides only a vague link to 'feature/body state' without mapping expectedFeatureCount, expectedBodyCount, or preflightId to concrete concepts. With 0% schema description coverage, the description fails to compensate by explaining what these counts mean or how the preflightId relates to a previous mutation.

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 a specific verb ('verify') and resource ('observed feature/body state after a mutation'), which clearly answers what the tool does and distinguishes it from the creation and editing siblings like nx_extrude and nx_chamfer. However, it does not explicitly name a sibling alternative or contrast itself, so it misses the full 5-point bar for sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'after a mutation' provides a clear temporal context for when to use this tool, implying it is the follow-up verification step to mutation operations like nx_extrude or nx_fillet. There is no explicit guidance on when not to use it or which sibling might be preferable (e.g., nx_list_features or nx_measure_body), so the guidance remains implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.