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FERS — structural FEM

by com.ferscloud
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Park a model, get a handle

stash_model

Park a model and get back a short handle (model_ref). Every authoring tool, and solve_model, accepts model_ref instead of model — so a multi-step build stops shipping the whole document through your context on every call, which on a real model is thousands of tokens per step. Tools called with a handle return the same handle, so the chain stays cheap. Handles are private to you and expire after 24 hours; they are NOT saved models and never appear in list_models (use save_model for that). Free.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model": "<model>"
    },
    "name": "stash_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameNoOptional label, purely to help you keep several drafts apart.
modelYesStringified FERS model JSON to park

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations only declare the safety profile (non-read-only, non-destructive, non-idempotent); the description adds the important traits an agent could not infer: handles are private, expire after 24 hours, never appear in list_models, and are echoed back by downstream tools to keep chains cheap. This is exactly the lifecycle and scoping context annotations cannot carry.

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?

Front-loaded with the core action and return value, then layered with the token-saving rationale, lifecycle caveats, and the save_model disambiguation. No sentence is filler and the ordering matches how an agent would need the information.

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?

For a 2-parameter tool with no output schema and non-obvious semantics (ephemeral handles vs. saved models), the description covers creation, consumption by other tools, expiration, privacy, and the save_model contrast. Nothing an agent needs to call or interpret it correctly is missing.

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 description coverage is 100%, so both parameters (model and name) are already documented. The description adds meaning about how model_ref flows into other tools, but nothing extra about the inputs themselves, so the baseline 3 applies.

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?

States a specific verb and resource ('Park a model') plus the concrete result ('get back a short handle (model_ref)'). It also names the sibling it is not ('NOT saved models ... use save_model for that'), so an agent can distinguish it from save_model without opening either schema.

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?

Explicitly describes the scenario that selects this tool: multi-step authoring where shipping the whole document each call costs thousands of tokens. It also names the alternative (save_model) for the persistent-model case, covering both when-to-use and when-not.

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

All FERS — structural FEM toolsInspect
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