Skip to main content
Glama
Axiomatic-AI

axiomatic-mcp

Official
by Axiomatic-AI

AxTidy3D_execute_code

Run Tidy3D simulations: execute Python code locally for free results, or submit to cloud to receive a cost estimate and get confirmation before starting the actual run.

Instructions

Execute Tidy3D code. Local operations (e.g. ModeSolver.solve()) run for free and return results synchronously via export(name, value). Code that calls submit_to_cloud(sim) instead uploads the simulation and returns a cost estimate (task_id, task_status='estimated', estimated_cost_flex_credits) WITHOUT starting the run — nothing is billed at this point. Show the estimated cost to the user and get their confirmation, then call start_simulation with the returned task_id to actually run it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesTidy3D Python code to execute

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.20

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations to lean on, the description carries the full behavioral disclosure burden. It states that local operations are free and synchronous, that cloud submission returns a cost estimate without starting the run, and that nothing is billed until start_simulation is called. This prevents the agent from mistakenly believing the simulation was launched.

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?

Four tightly written sentences with the purpose front-loaded. Each sentence adds distinct, necessary information: the action, local mode behavior, cloud mode behavior, and the required next step. No filler or redundancy.

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?

Despite having no output schema, the description compensates by specifying both return paths: synchronous local results via export, and the structured cost-estimate object with task_id, task_status, and estimated_cost_flex_credits. It also names the necessary follow-up tool, making the full workflow clear.

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?

The single code parameter already has a clear schema description ('Tidy3D Python code to execute'), so the baseline is high. The description adds further meaning by explaining what such code can contain, such as export(name, value) and submit_to_cloud(sim), and what those calls return.

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?

The description opens with a precise verb+resource statement, 'Execute Tidy3D code,' and then clearly explains the two execution modes: local synchronous execution and cloud submission via submit_to_cloud. This distinguishes it from the sibling start_simulation, which consumes the task_id produced here, and from code-generation siblings.

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?

The description explicitly explains when local execution is appropriate, what happens when code calls submit_to_cloud, and routes the agent to call start_simulation only after user confirmation. This gives the agent a concrete decision path and names the exact follow-up sibling tool.

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

Deploy Server

Other Tools