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186,743 tools. Last updated 2026-06-10 02:23

"Google Ads" matching MCP tools:

  • Browse individual decoded ads from Heista's corpus of real winning Meta/TikTok creative. Takes optional filters: vertical, creative_format, marketing_angle, hook_type, algo_intent, brand (partial name match), and limit (1-10, default 5). Each result returns beat timeline, classification, psychology, runtime performance signals (active days on Meta when available), and a decode id you can pass into generate_adscript with source_type="decode" to write a fresh script on that exact structure. Free, read-only, idempotent — no credits consumed. Use this when the user wants a specific ad as a script template (not an averaged formula), asks "show me winning ads in [vertical]", "what are [brand]'s top ads", or wants to see examples before committing to a generation. Source discovery surface — the response is the spine; for the full bundle with transcripts and director's read, call get_decode by id afterwards. Do NOT use to decode a NEW ad from a URL — use decode_ad (paid). Do NOT use for category-level patterns abstracted across multiple ads — use adformula_intelligence. Do NOT use to write the script itself — use generate_adscript or write directly from the bundle.
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  • Lists pre-configured reports (prebuilds) available for a connector. **What is a prebuild?** A prebuild is a standardized report maintained by Quanti for a given connector (e.g., Campaign Stats for Google Ads). It defines the BigQuery table structure (columns, types, metrics) and the associated API query. **When to use this tool:** - When the user asks "what reports are available for [connector]?" - When the user doesn't know which data or metrics exist for a connector - BEFORE get_schema_context, to explore available reports for a connector - To understand the data structure before writing SQL **Difference with get_schema_context:** - list_prebuilds → discover which reports/tables EXIST for a connector (catalog) - get_schema_context → get the actual BigQuery schema for the client project (effective data) **Response format:** Returns a JSON with for each prebuild: its ID, name, description, BigQuery table name, and the list of fields (name, type, description, is_metric). Fields marked is_metric=true are aggregatable metrics (impressions, clicks, cost...), others are dimensions (date, campaign_name...). **SKU examples**: googleads, meta, tiktok, tiktok-organic, amazon-ads, amazon-dsp, piano, shopify-v2, microsoftads, prestashop-api, mailchimp, kwanko
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  • Lists directly accessible Google Ads customers for the configured Google Ads credentials, including descriptive names when Google returns them. Use this to discover customer IDs before running Google Ads hierarchy or reporting tools.
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  • Initiate an OAuth handoff to a vendor integration (Google Ads, GA4, Search Console, Sheets, Drive, BigQuery, Meta Ads, Jira, Confluence). Returns an authorization URL the user opens in a browser. After the user clicks Allow, the connection is created and you can poll check_integration_status(handoff_id) to find out when the data is ready.
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  • User-facing render tool for Google Ads visual weekly reports. Use this directly for prompts like 'show me a Google Ads report', 'generate a Google Ads dashboard', or 'show 7/30/90-day Google Ads performance'. Do not first call google_ads_get_weekly_group_report unless you already need raw data for a non-visual answer; when this visual report renders, keep any assistant text to a brief confirmation.
    Connector

Matching MCP Servers

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    An MCP server that lets AI assistants look up any advertiser's Google ads. Search by domain or company name, retrieve ad creatives, and decode text ad content from Google's Ads Transparency Center.
    Last updated
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    MIT

Matching MCP Connectors

  • Google Ads automation with AI: analyze performance, manage campaigns, optimize bids.

  • Scrape Google search results with SERP data, ads, and knowledge panels

  • URL → clean, LLM-ready markdown (boilerplate/nav/ads stripped, headings + lists + links preserved) with a signed provenance receipt pinning the markdown to its source — the RAG-ingest primitive. Deterministic (no LLM): same URL + same source bytes ⇒ byte-identical markdown. — $0.005/call
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  • Resolves a list of Google Maps URLs into canonical Google Maps Place IDs. **When to call this tool (CRITICAL):** * Use this tool when the user provides one or more Google Maps sharing links or URLs (e.g. 'https://maps.app.goo.gl/...', 'https://www.google.com/maps/place/...', or 'https://maps.google.com/...') and you need to extract the underlying canonical Place IDs. * You can specify up to 20 URLs to resolve in a single batch request. **Input Requirements (CRITICAL):** * **`urls` (array of strings - MANDATORY):** The list of Google Maps URLs to resolve. Each URL must be a valid, single-place Google Maps URL. **Error Handling (CRITICAL):** * This is a batch processing tool. A request might return "mixed results" (e.g. some URLs resolve successfully while others fail). * The output list of `entities` is guaranteed to map 1:1 with the input `urls` indices. A failed URL resolution will result in an empty `Entity` message (no fields are set) at its corresponding index in the `entities` list. * You **MUST** check the `failed_requests` map field in the response to identify which specific URL index failed. The key of `failed_requests` represents the 0-based index of the failed URL in the request. Do not assume the entire batch call failed because of a partial failure.
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  • Get one decoded ads preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only.
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  • Get one static ads preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only.
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  • Preferred user-facing Google Ads search-terms analysis tool. Renders the search-terms analysis dashboard and can either take analysisPayload from google_ads_analyze_search_terms or fetch the analysis directly when called with search-term-analysis arguments.
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  • Data tool for the current user's MCP/Auth0, LinkedIn Ads, and Google Ads connection status plus the exact setup URLs to continue the flow in a browser. For the user-facing setup UI, prefer render_auth_setup_status.
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  • Step 2 — List data sources available within a tenant. (In the Indicate system a data source is called a 'data product'.) Examples: Google Analytics, Facebook Ads, vioma, Booking.com. Returns each data source's 'id', 'displayName', and 'semantic_context_id'. → Pass the chosen 'id' as 'data_source_id' and 'semantic_context_id' to list_metrics.
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  • Returns busy windows from YOUR connected Google calendar within a time window, plus free intervals of at least the requested minimum length. Use this to check your own availability before scheduling anything — gatherings, calls, anything. The 'busy' result is sourced directly from your Google calendar's freeBusy API; no event titles or details are returned, only the time ranges. Requires an active Google calendar connection (call lyra_connect_calendar first if you don't have one) and API key authentication. Returns a clear error if no calendar is connected.
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  • Get current ads scheduled for a device (for testing). WHEN TO USE: - Testing device ad delivery - Debugging which ads are being shown - Verifying ad targeting is working RETURNS: - ads: Array of advertisement objects - default_stream: Default content when no ads - schedule: Current ad schedule EXAMPLE: User: "What ads are showing on device P_abc123?" get_device_ads({ fingerprint: "P_abc123" })
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  • List configured review platforms (Google, Hipages, Facebook, etc) with their URLs. Useful for knowing where to direct review requests.
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  • Preferred user-facing cross-channel report tool. Use this directly when the user asks for one report, dashboard, or visual report covering both Google Ads and LinkedIn Ads, including prompts like 'generate a report for my Thorogood Google and LinkedIn ads for the past 7 days up to 25 May' or 'past 7 and 30 days up to 25 May'. It renders the combined weekly dashboard MCP app and accepts exact historical date ranges through endDate plus lookbackDays or windows, so do not fetch raw data first just to satisfy a historical week ending date. It can fetch fresh grouped data for the requested platforms and supports selectable report windows such as [7, 30]. Each selected window compares with its immediately preceding same-length period; do not compare 7 days against 30 days unless the user explicitly asks for that non-like-for-like analysis. When this widget renders, keep assistant text to a brief confirmation.
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  • Controlled Packrift Google Retail / AI Commerce Search sales test. Uses the imported Retail catalog to find likely buyer matches, returns AI_APPROVE-gated cart-handoff candidates, and records low-cap test attribution. Use this for the Gemini/Retail pilot before normal search_products when testing Google Retail search quality.
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  • Cluster-level structural formulas derived from decoded ads. Heista-curated; served as a generation parameter. Read-only, free. Filter scope with only_workspace / only_official (mutually exclusive — same toggle as the in-app library lens). Page with limit + offset.
    Connector