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search_competitor_ads

Read-onlyIdempotent

Search live competitor ads by keyword on Meta or TikTok: advertiser, copy, hook, call to action, media and run time per ad. Costs no credits. When live data is not available the rows are samples, marked demo: true with a note; never present those as real ads. SIGN-IN REQUIRED: connect this server with OAuth (the host prompts for it), or add an API key header "Authorization: Bearer acd_live_…" created at https://aicontentdrop.com/settings/integrations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoimpressions (default) ranks by reach; recent by start date.
limitNoRows to return, 1-20; defaults to 10.
keywordYesWhat to search for: a product, a category or a competitor, 1-200 characters.
platformNoDefaults to meta.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
demoYesTrue when results are sample rows because live data was not available.
noteNoPresent only when demo is true: why, and what the rows are.
sortYes
countYesRows returned.
keywordYes
resultsYes
platformYes
next_stepYes
total_foundYesRows the search produced before limit.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, non-destructive, open-world behavior, so the safety bar is covered; the description goes further by disclosing that fallback rows are synthetic and flagged demo: true, instructing the agent never to present them as real ads, and spelling out the auth path (OAuth or Authorization: Bearer acd_live_ key).

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?

Purpose is front-loaded in the first clause, followed by cost, data-quality caveat, and auth prerequisite in that priority order. Every sentence carries function; the auth URL is long but necessary and appears last.

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?

With an output schema present and 100% schema coverage, the description need not explain return values, and it covers cost, fallback data semantics, and authentication. The only gap is lack of comparative guidance against sibling ad tools.

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 keyword, platform, sort and limit are already fully documented in the schema; the description's mention of "by keyword on Meta or TikTok" only echoes that. No format or edge-case detail (e.g. keyword syntax caveats) is added beyond the 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?

States a specific verb (search) plus resource (competitor ads) with explicit scope: live ads by keyword on Meta or TikTok. It even enumerates the per-ad fields returned (advertiser, copy, hook, CTA, media, run time), which separates it cleanly from siblings like search_articles and list_saved_ads.

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

Usage Guidelines3/5

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

"Costs no credits" and the demo-sample caveat imply when this is worth calling, and the sign-in requirement is a clear precondition. However, it names no alternative tool (e.g. save_competitor_ad, list_saved_ads) and gives no explicit when-not-to-use guidance, so routing is left partly to inference.

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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