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jacksonnnzzz

leam-opt-mcp

by jacksonnnzzz

generate_antenna_pipeline

Converts antenna papers, diagrams, and natural language specs into an auditable HFSS Python pipeline, running source understanding and optimization planning before stopping at the artifact review gate.

Instructions

Run source understanding through optimization planning, then stop at the artifact review gate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations, the description alone must disclose side effects and behavior. It only mentions a stopping point ('stop at the artifact review gate') and a processing sequence, but does not say whether it mutates data, what inputs it uses beyond job_id, or whether it's long-running.

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 one short sentence, so it is not verbose, but it is under-specified for the complexity of the tool. It lacks any structural breakdown (e.g., steps, conditions) that would help an agent understand the pipeline behavior.

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 complexity implied by 21 sibling tools, this description is incomplete. It does not cover the output schema, the nature of the artifact review gate, or what 'source understanding' and 'optimization planning' entail.

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

Parameters1/5

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

The schema has a single required job_id with no description, and the tool description does not mention it. With 0% schema coverage, the description fails to explain the parameter's meaning or format.

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 gives a high-level process flow ('Run source understanding through optimization planning, then stop at the artifact review gate') but uses vague terms like 'source understanding' and 'optimization planning' without specifying the concrete action or output. It doesn't clearly state that it generates a pipeline, despite the tool name, and it doesn't differentiate from sibling pipeline steps.

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?

The description provides no guidance on when to call this tool versus the many sibling tools. It implicitly suggests a sequence but never explains prerequisites or alternatives.

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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