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femm_export_pyfemm_script

Export the current session journal as a standalone pyFEMM script so users can re-run the full finite element model without the AI agent.

Instructions

Write the session journal as a standalone pyFEMM script (import femm; femm.openfemm(1); femm.mi_...(); femm.closefemm()) so the user can re-run the whole model without the agent. Default path: <work_dir>/scripts/_pyfemm.py. femm_eval expressions that are not plain calls are kept as comments.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNo
sessionNomain
headlessNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses a lossy transformation ('femm_eval expressions that are not plain calls are kept as comments') and the default output location, which is real value beyond the schema. It does not state overwrite behavior for an existing file at the default path, error conditions, or what the generated script omits (e.g. measured results), which are the risks an agent needs to know about a file-writing tool.

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?

Three sentences, front-loaded with the core action, then the default path, then the edge case. The inline pyFEMM snippet is dense but earns its place by showing the emitted form. Little waste overall.

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

Completeness3/5

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

For a 3-parameter, no-annotation, no-output-schema tool with 0% schema coverage, the description is minimally adequate: it explains the artifact and the default destination but omits headless semantics, overwrite behavior, and any hint of the return/confirmation shape. An agent could call it correctly but not predict its side effects fully.

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?

Schema description coverage is 0%, so the description must compensate. It partly does: the default path template '<work_dir>/scripts/<session>_pyfemm.py' explains the role of both path and session. However, the 'headless' parameter is never mentioned, and it is the least self-explanatory of the three for an export tool, so roughly a third of the parameters remain undocumented.

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 and artifact: write the session journal as a standalone pyFEMM script with a sample of the emitted code. That is unambiguous and clearly distinct from femm_export_dxf or the generic femm_run_script/femm_run_lua, but no sibling is named, so differentiation is left implicit.

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 purpose clause 'so the user can re-run the whole model without the agent' implies the use case (handing a reproducible model to a user), but there is no explicit when-to-use statement, no exclusion, and no comparison against siblings such as femm_journal, femm_run_script, or femm_save that an agent might otherwise choose.

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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