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rugvedp

All About Ads MCP

start_google_ads_scrape

Initiate a background Google Ads scrape, returning a run ID instantly. Use for parallel scraping; monitor with check_run_status and collect results on success.

Instructions

Start a Google Ads Transparency Center scrape in the background and return a run_id immediately.

Use this instead of search_google_ads when running multiple scrapers in parallel. Call check_run_status to monitor progress, then collect_scrape_results once SUCCEEDED.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
regionNo2-letter ISO country code to filter by, e.g. 'US', 'GB'. None for worldwide.
end_dateNoLatest last-shown date in YYYY-MM-DD format. None for no upper bound.
start_dateNoEarliest first-shown date in YYYY-MM-DD format. None for no lower bound.
advertisersYesBrand names, domains (e.g. 'nike.com'), full URLs, or advertiser IDs starting with 'AR'. Mix formats freely.
political_ads_onlyNoRestrict results to political and election ads only.
max_ads_per_advertiserNoMaximum ads returned per advertiser. 0 means unlimited.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hintYes
run_idYes
statusYes
Behavior4/5

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

The description adds behavioral context beyond annotations: it explains the asynchronous, background nature and the pattern of polling for results. Annotations only indicate mutability (readOnlyHint=false) and variability (openWorldHint=true); the description elaborates well without 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?

The description is three sentences with no wasted words. It front-loads the action, then provides usage guidance and workflow, achieving perfect conciseness.

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?

Given the complexity (6 parameters, 1 required, output schema exists), the description is complete: it explains what the tool does, when to use it, and the subsequent steps needed. It does not need to detail output schema since that is provided separately.

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 baseline is 3. The description does not add additional parameter meaning beyond what is already in the schema, so it meets the baseline but does not exceed.

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 the tool starts a Google Ads Transparency Center scrape in the background and returns a run_id. It also distinguishes from the sibling tool search_google_ads, meeting the highest standard for purpose clarity.

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?

The description explicitly says 'Use this instead of search_google_ads when running multiple scrapers in parallel,' providing a clear alternative and the parallel use case. It also specifies the workflow of calling check_run_status and collect_scrape_results.

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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