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engineering_compare_model_versions

Compare engineering model versions by specifying a free-text objective or structured JSON inputs. Integrates with AI coding assistants via Lightbulb Partners.

Instructions

Run the engineering domain agent action compare_model_versions.

Routes through the platform's domain-agent dispatcher under your JWT, tenant, and company scope.

Args: message: Free-text objective for the action. inputs: Optional JSON string of structured inputs for the action.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageNo
inputsNo{}

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It only mentions routing through a dispatcher with JWT/tenant/company scope, but fails to state whether the action is read-only, destructive, or idempotent. No side effects or return behavior are described.

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

Conciseness3/5

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

The description is relatively short but includes technical routing details that are likely unnecessary for the agent. The first sentence is nearly tautological, and the second sentence adds implementation detail rather than core purpose.

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?

For an engineering tool that compares model versions, the description is minimal. It does not describe what 'model versions' are, what the output looks like, or any constraints. Given the complexity and the presence of output schema, more context is needed to avoid confusion with tools like engineering_run_clash_analysis.

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 coverage is 0%, so the description must compensate. It adds meaning by describing 'message' as 'Free-text objective' and 'inputs' as 'Optional JSON string of structured inputs'. While helpful, it does not specify the expected structure of the JSON or provide examples, leaving significant ambiguity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Run the engineering domain agent action compare_model_versions', which is nearly tautological but includes the verb 'compare' and resource 'model versions'. However, it does not clarify what types of models are being compared (e.g., ML models, CAD models) and lacks context that distinguishes it from other engineering tools.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool vs. alternatives. There is no mention of prerequisites, when not to use it, or any comparison to siblings like engineering_query_analytics or engineering_design_loop.

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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