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tracetify

tracetify-mcp

Official

Competitor ads (Meta + Google)

research_competitor_ads

Pull a domain's live Facebook, Instagram, and Google ads, including copy, CTAs, landing pages, creatives, platforms, and start dates for competitor research.

Instructions

The ads a domain is running right now on Facebook/Instagram (Meta Ad Library) and Google (Ads Transparency Center): headline, body copy, CTA, landing page URL, creative/video URL, platforms and start dates. Use when the user asks what a competitor is advertising, whether they run paid ads, how long an ad has been live, or wants hooks and angles to model. Costs credits; the same domain re-queried within a week is free. Each source reports separately — null means that source could not be checked, an empty list means it was checked and found nothing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesDomain whose ads to pull, e.g. heavys.com

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.6.0

TDQS

A4.6/5.0
Behavior5/5

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

Goes well beyond annotations by disclosing credit cost, a one-week cache exemption, per-source independent reporting, and the crucial null-vs-empty-list distinction (source unchecked vs checked-and-empty). That last point prevents a common misinterpretation of results.

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 with the resource and returned fields, then usage triggers, then cost/caching, then result semantics. Dense but every sentence carries information; the returned-field enumeration is slightly list-like but justified by the absent output schema.

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?

No output schema exists, so the description compensates by enumerating returned fields and, importantly, explaining the null vs empty-list return semantics. Nothing an agent needs to call or interpret this tool is missing.

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?

Only one parameter and schema description coverage is 100%, so the schema already documents 'domain' with an example. The description adds nothing about the parameter itself (e.g., subdomain handling), so the baseline 3 applies.

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?

Specific verb+resource with explicit scope: it names both sources (Meta Ad Library, Google Ads Transparency Center) and enumerates the returned fields. An agent can distinguish it from research_competitors or research_brand_lookup without opening any schema.

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?

Explicit 'Use when the user asks what a competitor is advertising, whether they run paid ads, how long an ad has been live, or wants hooks and angles to model.' It also discloses the cost model and the free-re-query window, which materially affects invocation decisions.

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