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Export HUMMBL Models

export_models

Export curated Base120 mental models as Markdown, JSON, or PDF. Filter by specific codes, transformation group, or export all 120 models.

Instructions

Export a curated subset of Base120 mental models as Markdown, JSON, or PDF. Pass codes for a specific list, transformation for a whole group, or neither for all 120.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatYesOutput format: 'markdown', 'json', or 'pdf'.
codesNoOptional list of model codes (e.g. ['P1','IN3','CO5']). Takes precedence.
transformationNoOptional transformation key (P, IN, CO, DE, RE, SY) to export a whole group.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatYes
modelCountYes
missingCodesYes
contentYes
byteLengthNo
Behavior3/5

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

No annotations are provided, so the description bears the burden of behavioral transparency. It states the action is 'Export' which implies a read-only operation, but it does not explicitly confirm side-effect-free behavior, authentication requirements, or any potential limitations. The description is adequate but lacks explicit safety or behavioral context beyond the obvious.

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?

The description is two sentences with no extraneous information. The first sentence states the core function and formats, the second explains parameter usage. Every word is necessary and front-loaded.

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?

Given the presence of an output schema (indicated by the signal), the description does not need to detail return values. It covers all selection modes and required input. Minor omission: no mention of limits on the number of codes, but not critical for a curated subset export.

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 100%, but the description adds significant value by explaining the interaction between the optional parameters (codes takes precedence, transformation vs. all). This clarifies the logic beyond what individual parameter descriptions provide.

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 clearly states that the tool exports mental models in three formats (Markdown, JSON, PDF) and distinguishes three modes of selection (codes list, transformation group, or all 120). This verb+resource+variation is specific and differentiates from sibling tools which focus on relationships, recommendations, etc.

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?

The description explicitly explains when to use each parameter: 'Pass `codes` for a specific list, `transformation` for a whole group, or neither for all 120.' This provides clear context. It does not explicitly exclude sibling tools, but the sibling set is diverse enough that this is the only export tool, so no confusion is likely.

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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