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Heista

by co.heista

List Models

list_talent_model_presets
Read-onlyIdempotent

Saved casting talent — a person you can re-use across Heists. The Models Heist saves them on click; future Heists can pick one as a brand-aware talent reference. Workspace = your saved castings. Official = Heista-curated drops across fashion, lifestyle, everyday, character, and creator buckets. Read-only, free. Filter scope with only_workspace / only_official (mutually exclusive — same toggle as the in-app library lens). Page with limit + offset.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoPage size. Default 24, max 100.
offsetNoOffset for paging through results. Default 0.
brand_idNoOptional brand_id to scope workspace presets to. Get from list_brands. Official presets are not brand-scoped and are unaffected by this filter.
only_officialNoWhen true, hide workspace (user-created) presets and only return Heista-curated (official) presets. Mutually exclusive with only_workspace.
only_workspaceNoWhen true, hide Heista-curated (official) presets and only return workspace (user-created) presets. Mutually exclusive with only_official.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint. The description adds value by stating 'Read-only, free' and explaining the mutual exclusivity of boolean filters and brand_id scope behavior. No contradictions with annotations.

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?

The description is moderately long but well-structured, starting with the core purpose and then adding details. Every sentence adds value, though it could be slightly more concise. No fluff; appropriate length for the concept.

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 absence of an output schema, the description provides sufficient context: what the list contains (workspace and official presets), filtering options, and pagination. It does not mention response structure or default ordering, but the high schema coverage and annotations compensate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers all parameters with descriptions (100% coverage). The description adds meaning by explaining that only_workspace and only_official are mutually exclusive, mirror the in-app library lens, and that brand_id only scopes workspace presets.

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 defines the tool as listing saved casting talent (workspace and official presets) with specific verb 'list' and resource description. It distinguishes itself from sibling list tools by detailing the two types of presets and their filtering scope.

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 provides clear usage context: it states the tool is read-only and free, explains the mutually exclusive filters only_workspace and only_official, and notes brand_id scoping only affects workspace presets. It lacks explicit 'when not to use' vs. other list tools, but the guidance is sufficient for typical use.

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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TDQS

A3.7/5.0
Disambiguation4/5

Despite the high tool count, most tools have distinct purposes with thorough descriptions that specify when to use each. Some overlap exists among creative direction tools (call_creative_worlds vs chat_with_creative_worlds), but the descriptions clarify usage patterns.

Naming Consistency3/5

Naming conventions are inconsistent overall: some follow verb_noun (create_powersource_url, decode_ad), others use noun_verb or compound names (adformula_intelligence, fleet_analytics_overview). However, subgroups like dispatch_* and list_*_presets maintain internal consistency.

Tool Count2/5

112 tools is far beyond the typical 3-15 range for well-scoped servers. While the server covers a broad domain, the sheer number likely overwhelms agents and suggests insufficient consolidation of related operations.

Completeness4/5

The tool set covers core creative intelligence workflows: brand analysis, ad decoding, script generation, creative direction, and research. Minor gaps exist (e.g., no social media publishing tools), but the main use cases are well-supported.

Resources