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by co.heista

Fleet — Search Decoded Ads

fleet_search_decoded_ads
Read-onlyIdempotent

Search the published Ad Intelligence corpus (the public decode gallery). Filter by free-text (name/tagline/brand), brand, category, vertical, or platform. Returns list rows with public URLs — never the full structural payload (use fleet_get_decoded_ad for that). Hard cap 50 rows per call; paginate with offset. Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandNoFilter by brand name (partial match).
limitNoMax rows (default 10, hard cap 50).
queryNoFree-text match against ad name, tagline, and brand name.
offsetNoPagination offset.
categoryNoFilter by gallery category (exact match — values from fleet_intel_stats / prior searches).
platformNoFilter by platform (e.g. facebook, tiktok).
verticalNoFilter by vertical classification (exact match).

TDQS

A4.7/5.0
Behavior5/5

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

Discloses return format (list rows with public URLs), hard limit 50, pagination requirement, and read-only behavior. Annotations already indicate readOnlyHint, idempotentHint, no contradiction.

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?

Two sentences: first states purpose and filters, second details limitations and alternatives. Extremely concise and well-structured.

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?

Fully covers return format, pagination, limits, read-only, and alternative tool. With 7 optional parameters and no output schema, description is sufficient for correct invocation.

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 description adds minimal value beyond summarizing filter types. No new parameter details beyond schema.

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?

Description clearly states the tool searches the published Ad Intelligence corpus and lists filters (free-text, brand, category, vertical, platform). It distinguishes from fleet_get_decoded_ad by noting it returns list rows with public URLs, not full payload.

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 states when not to use (for full structural payload, use fleet_get_decoded_ad), hard cap 50 rows, paginate with offset, and read-only nature.

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