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google_ads_landing_page_analyze

Extract structured content from a landing page—title, meta description, headings, CTAs, features, prices, and JSON-LD—for ad-copy alignment and keyword analysis. Fetches via HTTP(S) with security protections.

Instructions

Fetch a landing page over HTTP(S) and extract structured content for ad-copy alignment. Returns title, meta_description, h1_texts, h2_texts, main_text (truncated to 1500 chars), cta_texts, features (list-item snippets, capped at 30), prices (JP yen patterns), brand_name, industry_hints, og_title, og_description, and structured_data (up to 5 JSON-LD blocks). On fetch or parse failure, returns the same shape with an error field set instead of raising. Side effect: issues one outbound HTTP GET to the URL with a 15s timeout, a 500KB body cap, up to 5 redirects, and a 'MarketingAgent/1.0' User-Agent; SSRF-protected against localhost, private / link-local / reserved IP ranges, and cloud metadata endpoints (redirect targets are re-validated). The Google Ads customer context is unused by the analysis itself — passing customer_id only scopes credential routing. Use this for ad-copy vs. LP message-match and keyword-extraction workflows. For Google's indexing/coverage view of the same URL use search_console_url_inspection_inspect; for a batched workflow that combines LP analysis with existing ads, search terms, and keyword suggestions use google_ads_creative_research.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
customer_idNoGoogle Ads customer ID as a 10-digit string without dashes (e.g. '1234567890'). Optional — falls back to GOOGLE_ADS_CUSTOMER_ID / GOOGLE_ADS_LOGIN_CUSTOMER_ID from the configured credentials when omitted.
urlYesAbsolute landing page URL to fetch (http:// or https:// scheme only, e.g. 'https://example.com/lp/offer'). Private-range, loopback, and cloud-metadata hosts are rejected.
Behavior5/5

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

Although no annotations are provided, the description details side effects (outbound HTTP GET with timeout, body cap, redirects, User-Agent), SSRF protections, error handling (returns error field on failure), and the fact that customer_id is unused for analysis. This is thorough behavioral disclosure.

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 informative and well-structured, starting with the primary purpose, then output fields, error handling, side effects, security, and usage guidance. Although slightly dense, every sentence adds value without redundancy.

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?

For a tool with no annotations and no output schema, the description provides a complete picture: purpose, extracted fields, error behavior, side effects, security considerations, parameter nuances, and usage context with alternatives. It leaves no major gaps.

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

Parameters4/5

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

Schema coverage is 100% with both parameters described. The description adds meaningful context beyond the schema: customer_id is only for credential routing and not used in analysis, and URL SSRF restrictions are reaffirmed. This justifies above baseline.

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 fetches a landing page and extracts structured content for ad-copy alignment. It distinguishes itself from sibling tools by referencing them and specifying different use cases.

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 describes when to use this tool (ad-copy vs LP message-match, keyword extraction) and provides clear alternatives: search_console_url_inspection_inspect for indexing view, google_ads_creative_research for batched workflows.

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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