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save_pattern

Store a reusable modeling pattern after a non-trivial approach succeeds, so you can recall it later with its name, description, code, and tags.

Instructions

Store a reusable modeling pattern for later recall; call this after a non-trivial approach worked.

Args:
    name: Short name, e.g. "flanged pipe via loft".
    description: What it does and when to use it.
    code: Optional Python snippet that implements it.
    tags: Retrieval tags.

Returns: The stored pattern_id.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`code`	No
`name`	Yes
`tags`	No
`description`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv0.7.7Oct 7, 2026
- addedInput schema / properties / description / minLength
```
Added value: +1
```
- addedInput schema / properties / name / minLength
```
Added value: +1
```
First observedv0.4.0Jul 31, 2026

TDQS

A3.8/5.0

Behavior3/5

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

Annotations already declare the mutation profile (readOnlyHint=false, idempotentHint=false, destructiveHint=false, openWorldHint=true), so the safety story is covered. The description adds that the pattern is stored 'for later recall' and returns a pattern_id, but says nothing about persistence scope (session vs. global), duplicate handling despite non-idempotency, or storage limits.

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?

Front-loaded one-line purpose followed by a tight Args block and a single Returns line; every sentence earns its place. The Args formatting is slightly boilerplate-heavy for four short fields, but nothing is wasted.

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

Completeness4/5

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

An output schema exists, so the brief Returns line is a bonus rather than a necessity, and all four parameters plus the use trigger are covered. The remaining gap is behavioral: an agent cannot tell whether saved patterns persist across sessions or whether repeated saves create duplicates.

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?

Schema description coverage is 0%, so the description carries the burden and mostly does: it explains all four parameters, including an example for name, the intent of description, that code is an optional Python snippet, and that tags drive retrieval. It does not clarify the tags format or length constraints, but it is well above the low-coverage baseline.

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?

States a specific verb and resource ('Store a reusable modeling pattern for later recall') and even gives an example name, so the operation is unmistakable. It implies its counterpart (recall_patterns) via 'for later recall' but never names it, so sibling differentiation is only implicit.

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

Usage Guidelines4/5

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

'call this after a non-trivial approach worked' gives a concrete trigger condition for use. It stops short of stating when not to save (trivial cases, duplicates) or explicitly pointing at recall_patterns as the retrieval counterpart.

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