"google" matching MCP tools:

• askSupport

  NotFair-MetaAds

  Contact NotFair support. Use this tool when the user explicitly wants to reach the support team — for example, they say "contact support", "file a bug", "report an issue", "I need help from the NotFair team", or "this is a NotFair problem not a Google Ads problem". This sends a message directly to the NotFair team and generates a ticket. The user will receive a response via email within 1 business day. DO NOT use this for: - Routine Google Ads questions you can answer yourself. - Internal tool quality issues — use fileInternalNotFairToolFeedback for those. - Questions you haven't tried to answer yet. Only call this when the user has explicitly asked to contact support, or when you've exhausted your ability to help and the user agrees escalation is the right move.

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• generate_image

  NotFair-MetaAds

  Generate one image from a prompt using OpenAI GPT Image 2. Returns a public URL you can embed in markdown or pass to a creative-asset tool (e.g. Google Ads `createImageAsset`). Counts against the user's monthly quota. Prompt craft (GPT Image 2 rewards long, specific, instruction-style prompts — write a paragraph, not keywords): - Lead with the medium: photograph, 3D render, isometric vector, watercolor, flat illustration, studio product shot. Single biggest quality lever. - Then specify subject, setting, mood, color palette, lighting (e.g. 'golden hour, soft backlight'), and camera/perspective (close-up, wide, overhead, low angle, macro). - Keep the focal subject in the center 80% of the frame — ad platforms crop edges across placements. - Prefer lifestyle / in-context scenes over isolated-on-white product shots. Google explicitly recommends 'physical settings with organic shadows and lighting' for ad creative. - Don't render text unless the user asks for specific copy. Overlaid text is often unreadable at small ad sizes and Google flags it as a quality issue. - Avoid negative prompts ('no X, no Y'). GPT Image often pulls the rejected concept in — describe what you want instead. Ad-policy rules to bake into prompts: - No collages, borders, watermarks, mirrored / skewed / over-filtered looks. - No fake UI elements (play buttons, download/close icons) — Google Ads policy violation. - Don't overlay a logo on the photo; logos belong inside the scene (on a product, sign, storefront). - Blank space should be under 80% of the frame — the subject is the focus. Aspect ratios — match the target placement: - Google Ads asset slots: '1.91:1' landscape (required), '1:1' square (required), '4:5' portrait, '9:16' vertical (Demand Gen / Shorts). - Meta / social: '1:1' or '4:5' feed; '9:16' stories/reels; '1.91:1' link previews. - Hero / web banners: '16:9' or '3:2'. Default is '1:1'. Quality vs latency: 'low' ~5s drafts; 'medium' balanced; 'high' runs the four-stage Understand/Plan/Generate/Review pipeline (30–50× slower than low) — use only for production-final fidelity. Output format: default 'png' (lossless). Use 'webp' or 'jpeg' for smaller photographic assets. background='transparent' requires png/webp (use for logos, cutouts, UI assets).

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• get_domain_senders

  DMARKOFF — DMARC, SPF & DKIM Analytics

  List top sending sources (ESPs, ISPs, mail services) for a domain, grouped by source type. Filters: "known" (legitimate ESPs like Google, Mailgun), "unknown" (unrecognized senders), "forward" (forwarding services). Empty = all types. Returns top 20 per type with message volume, SPF/DKIM/DMARC pass/fail counts. Use this to investigate WHERE email is being sent from — especially when unknown sources appear or compliance is low. To drill down into a specific source (by IP, ISP, hostname, or reporter), use get_domain_source_details.

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• lookup_weather

  Google Maps

  Retrieves comprehensive weather data including current conditions, hourly, and daily forecasts. **Specific Data Available:** Temperature (Current, Feels Like, Max/Min, Heat Index), Wind (Speed, Gusts, Direction), Celestial Events (Sunrise/Sunset, Moon Phase), Precipitation (Type, Probability, Quantity/QPF), Atmospheric Conditions (UV Index, Humidity, Cloud Cover, Thunderstorm Probability), and Geocoded Location Address. **Location & Location Rules (CRITICAL):** The location for which weather data is requested is specified using the `location` field. This field is a 'oneof' structure, meaning you MUST provide a value for ONLY ONE of the three location sub-fields below to ensure an accurate weather data lookup. 1. Geographic Coordinates (lat_lng) * Use it when you are provided with exact lat/lng coordinates. * Example: {"location": {"lat_lng": {"latitude": 34.0522, "longitude": -118.2437}}} // Los Angeles 2. Place ID (place_id) * An unambiguous string identifier (Google Maps Place ID). * The place_id can be fetched from the search_places tool. * Example: {"location": {"place_id": "ChIJLU7jZClu5kcR4PcOOO6p3I0"}} // Eiffel Tower 3. Address String (address) * A free-form string that requires specificity for geocoding. * City & Region: Always include region/country (e.g., "London, UK", not "London"). * Street Address: Provide the full address (e.g., "1600 Pennsylvania Ave NW, Washington, DC"). * Postal/Zip Codes: MUST be accompanied by a country name (e.g., "90210, USA", NOT "90210"). * Example: {"location": {"address": "1600 Pennsylvania Ave NW, Washington, DC"}} **Usage Modes:** * **Current Weather:** Provide `location` only. Do not specify `date` and `hour`. * **Hourly Forecast:** Provide `location`, `date`, and `hour` (0-23). Use for specific times (e.g., "at 5 PM") or terms like "next few hours" or "later today". If the user specifies minute, round down to the nearest hour. Hourly forecast beyond 120 hours from now is not supported. Historical hourly weather is supported up to 24 hours in the past. * **Daily Forecast:** Provide `location` and `date`. Do not specify `hour`. Use for general day requests (e.g., "weather for tomorrow", "weather on Friday", "weather on 12/25"). If today's date is not in the context, you should clarify it with the user. Daily forecast beyond 10 days including today is not supported. Historical weather is not supported. **Parameter Constraints:** * **Timezones:** All `date` and `hour` inputs must be relative to the **location's local time zone**, not the user's time zone. * **Date Format:** Inputs must be separated into `{year, month, day}` integers. * **Units:** Defaults to `METRIC`. Set `units_system` to `IMPERIAL` for Fahrenheit/Miles if the user implies US standards or explicitly requests it. * The grounded output must be attributed to the source using the information from the `attribution` field when available.

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• get_url_report

  Peec AI MCP Server

  Get a report on source URL visibility and citations across AI search engines. Results are aggregated for the entire date range by default. Use the "date" dimension for daily breakdowns. Returns columnar JSON: {columns, rows, rowCount}. Each row is an array of values matching column order. Columns: - url: the full source URL (e.g. "https://example.com/page") - classification: page type — Homepage, Category Page, Product Page, Listicle (list-structured articles), Comparison (product/service comparisons), Profile (directory entries like G2 or Yelp), Alternative (alternatives-to articles), Discussion (forums, comment threads), How-To Guide, Article (general editorial content), Other, or null - title: page title or null - channel_title: channel or author name (e.g. YouTube channel, subreddit) or null - citation_count: total number of explicit citations across all chats - retrieval_count: total number of distinct chats that retrieved this URL, regardless of whether it was cited - citation_rate: average number of inline citations per chat when this URL is retrieved. Can exceed 1.0 — higher values indicate more authoritative content. - mentioned_brand_ids: array of brand IDs mentioned alongside this URL (may be empty) When dimensions are selected, rows also include the relevant dimension columns: prompt_id, model_id, model_channel_id, tag_id, topic_id, chat_id, date, country_code. Dimensions explained: - prompt_id: individual search queries/prompts - model_id: AI search engine (e.g. chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, deepseek-v4-pro, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4, qwen-3-6-plus, amazon-rufus-scraper) — deprecated, prefer model_channel_id - model_channel_id: stable engine channel (e.g. openai-0, openai-1, qwen-0, openai-2, perplexity-0, perplexity-1, google-0, google-1, google-2, google-3, anthropic-0, anthropic-1, deepseek-0, meta-0, xai-0, xai-1, microsoft-0, amazon-0) — survives model upgrades - tag_id: custom user-defined tags - topic_id: topic groupings - date: (YYYY-MM-DD format) - country_code: country (ISO 3166-1 alpha-2, e.g. "US", "DE") - chat_id: individual AI chat/conversation ID Filters use {field, operator, values} where operator is "in" or "not_in". Filterable fields: model_id (deprecated), model_channel_id, tag_id, topic_id, prompt_id, domain, domain_classification, url, url_classification, country_code, chat_id, mentioned_brand_id. Additional filters: - mentioned_brand_count: {field: "mentioned_brand_count", operator: "gt"|"gte"|"lt"|"lte", value: <number>} — filter by number of unique brands mentioned. - gap: {field: "gap", operator: "gt"|"gte"|"lt"|"lte", value: <number>} — gap analysis filter. Excludes URLs where the project's own brand is mentioned, and filters by the number of competitor brands present. Example: {field: "gap", operator: "gte", value: 2} returns URLs where the own brand is absent but at least 2 competitors are mentioned. Sort results with order_by: array of {field, direction} entries. Direction defaults to desc. Sortable fields: retrieval_count, retrievals, citation_count, citation_rate. Multiple entries create a multi-key sort.

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• hasdata_google_maps_photos_getMapPhotos

  HasData

  Get Place Photos Fetches the photo gallery of a Google Maps place by dataId or placeId, paginated with nextPageToken and filterable by categoryId (all, latest, menu, by owner, videos, street view). Returns each photo with image URL, thumbnail, upload date, uploader, and photoId. Use for restaurant-menu extraction, venue/ambience visual audits, building rich place detail pages, and sourcing up-to-date imagery for POI listings.

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• get_domain_report

  Peec AI MCP Server

  Get a report on source domain visibility and citations across AI search engines. Results are aggregated for the entire date range by default. Use the "date" dimension for daily breakdowns. Returns columnar JSON: {columns, rows, rowCount}. Each row is an array of values matching column order. Columns: - domain: the source domain (e.g. "example.com") - classification: domain type — Corporate (official company sites), Editorial (news, blogs, magazines), Institutional (government, education, nonprofit), UGC (social media, forums, communities), Reference (encyclopedias, documentation), Competitor (direct competitors), You (the user's own domains), Other, or null - retrieved_percentage: 0–1 ratio — fraction of chats that included at least one URL from this domain. 0.30 means 30% of chats. - retrieval_rate: average number of URLs from this domain pulled per chat. Can exceed 1.0 — values above 1.0 mean multiple pages from the same domain are retrieved per conversation. - citation_rate: average number of inline citations when this domain is retrieved. Can exceed 1.0 — higher values indicate stronger content authority. - retrieval_count: total number of distinct URL retrievals from this domain across all chats (raw count — numerator of retrieval_rate). - citation_count: total number of citations from this domain (raw count). - mentioned_brand_ids: array of brand IDs mentioned alongside URLs from this domain (may be empty) When dimensions are selected, rows also include the relevant dimension columns: prompt_id, model_id, model_channel_id, tag_id, topic_id, chat_id, date, country_code. Dimensions explained: - prompt_id: individual search queries/prompts - model_id: AI search engine (e.g. chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, deepseek-v4-pro, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4, qwen-3-6-plus, amazon-rufus-scraper) — deprecated, prefer model_channel_id - model_channel_id: stable engine channel (e.g. openai-0, openai-1, qwen-0, openai-2, perplexity-0, perplexity-1, google-0, google-1, google-2, google-3, anthropic-0, anthropic-1, deepseek-0, meta-0, xai-0, xai-1, microsoft-0, amazon-0) — survives model upgrades - tag_id: custom user-defined tags - topic_id: topic groupings - date: (YYYY-MM-DD format) - country_code: country (ISO 3166-1 alpha-2, e.g. "US", "DE") - chat_id: individual AI chat/conversation ID Filters use {field, operator, values} where operator is "in" or "not_in". Filterable fields: model_id (deprecated), model_channel_id, tag_id, topic_id, prompt_id, domain, domain_classification, url, country_code, chat_id, mentioned_brand_id. Additional filters: - mentioned_brand_count: {field: "mentioned_brand_count", operator: "gt"|"gte"|"lt"|"lte", value: <number>} — filter by number of unique brands mentioned. - gap: {field: "gap", operator: "gt"|"gte"|"lt"|"lte", value: <number>} — gap analysis filter. Excludes domains where the project's own brand is mentioned, and filters by the number of competitor brands present. Example: {field: "gap", operator: "gte", value: 2} returns domains where the own brand is absent but at least 2 competitors are mentioned. Sort results with order_by: array of {field, direction} entries. Direction defaults to desc. Sortable fields: citation_rate, retrieval_count, citation_count. (retrieved_percentage and retrieval_rate are not sortable because they depend on totalChatCount fetched in a separate query.)

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• hasdata_google_serp_immersive_product_getImmersive_e29f691177

  HasData

  Get Immersive Product Information Expands the Google Shopping Immersive Product pop-up given an immersiveProductPageToken from the Google Shopping API, with optional moreStores (up to ~13 merchants instead of 3–5) and nextPageToken for paginating stores. Returns multi-store offers (merchant, price, shipping, condition, URL), product specs, images, ratings, and the nextPageToken. Use for price-comparison bots, merchant discovery, dropshipping research, and aggregating full offer lists per product.

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• list_data_sources

  Indicate

  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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• delete_instance

  mcp

  Delete a Google Compute Engine virtual machine (VM) instance. Requires project, zone, and instance name as input. Proceed only if there is no error in response and the status of the operation is `DONE` without any errors. To get details of the operation, use the `get_zone_operation` tool.

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• get_news_articles

  TensorFeed

  Get the latest AI news articles aggregated from 12+ sources (Anthropic, OpenAI, Google, HuggingFace, TechCrunch, The Verge, Hacker News, etc). Polled every 10 min, deduplicated, sanitized for prompt injection. Returns up to 200 articles with title, snippet, source, and publishedAt.

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• google_flights_location_search

  Searchapi

  Search for airports and cities to get their identifiers for Google Flights tools. Returns: - IATA airport codes (e.g., 'JFK') for specific airports - kgmid (e.g., '/m/02_286') for cities - searches all airports in that city Use this tool when you have a city name like 'New York' or 'Paris' and need to convert it to codes that the flight tools accept. Note: Common IATA codes like JFK, LAX, SFO, LHR, CDG, NRT can be used directly without this tool.

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• compute_routes

  Google Maps

  Computes a travel route between a specified origin and destination. **Supported Travel Modes:** DRIVE (default), WALK. **Input Requirements (CRITICAL):** Requires both **origin** and **destination**. Each must be provided using one of the following methods, nested within its respective field: * **address:** (string, e.g., 'Eiffel Tower, Paris'). Note: The more granular or specific the input address is, the better the results will be. * **lat_lng:** (object, {"latitude": number, "longitude": number}) * **place_id:** (string, e.g., 'ChIJOwE_Id1w5EAR4Q27FkL6T_0') Note: This id can be obtained from the search_places tool. Any combination of input types is allowed (e.g., origin by address, destination by lat_lng). If either the origin or destination is missing, **you MUST ask the user for clarification** before attempting to call the tool. **Example Tool Call:** {"origin":{"address":"Eiffel Tower"},"destination":{"place_id":"ChIJt_5xIthw5EARoJ71mGq7t74"},"travel_mode":"DRIVE"} * The grounded output must be attributed to the source using the information from the `attribution` field when available.

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• list_chats

  Peec AI MCP Server

  List chats (individual AI responses) for a project over a date range. Each chat is produced by running one prompt against one AI engine on a given date. Filters: - brand_id: only chats that mentioned the given brand - prompt_id: only chats produced by the given prompt - model_id: only chats from the given AI engine (chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, deepseek-v4-pro, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4, qwen-3-6-plus, amazon-rufus-scraper) — deprecated, prefer model_channel_id - model_channel_id: only chats from the given engine channel (openai-0, openai-1, qwen-0, openai-2, perplexity-0, perplexity-1, google-0, google-1, google-2, google-3, anthropic-0, anthropic-1, deepseek-0, meta-0, xai-0, xai-1, microsoft-0, amazon-0) If both model_id and model_channel_id are provided, model_channel_id takes precedence and model_id is ignored. Use the returned chat IDs with get_chat to retrieve full message content, sources, and brand mentions. Returns columnar JSON: {columns, rows, rowCount, totalCount}. rowCount is the rows in this page; totalCount is the total matching records ignoring limit/offset. Columns: id, prompt_id, model_id, model_channel_id, date.

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• get_dashboard

  mcp

  Use this as the primary tool to retrieve a single specific custom monitoring dashboard from a Google Cloud project using the resource name of the requested dashboard. Custom monitoring dashboards let users view and analyze data from different sources in the same context. This is often used as a follow on to list_dashboards to get full details on a specific dashboard.

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• bing_search

  Searchapi

  Search the web using Bing. Returns organic results, related searches and more. Alternative to Google for web search with different ranking algorithms and results.

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• search_local_businesses

  google-maps-mcp-server

  Search Google Maps for local businesses matching a query and location. Returns business name, complete address, star rating, review count, phone number, website URL, and business category. Use for restaurant discovery, service provider lookup, or competitive local analysis. Returns open/closed status.

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• deploy_service_from_archive

  Google Cloud Run

  Deploy a Cloud Run service directly from a self-contained source code archive (.tar.gz), skipping the container image build step for faster deployment. The archive must include all dependencies: - For compiled languages (Go, Java), include pre-compiled binaries. - For scripting languages (Python, Node.js), include pre-installed libraries (e.g., vendor/, node_modules/). Deployment steps: 1. Package source code and dependencies into a .tar.gz archive (max 250MiB). It's recommended to create archive from the root of the application's source directory. 2. Upload the archive to a Google Cloud Storage bucket, preferably in the same region as the service. 3. Deploy to Cloud Run using this tool, specifying: - source_code: Google Cloud Storage object path to the archive (e.g., gs://bucket/object). - command: Command to start the application. - base_image_uri: Base image for the container (e.g., go124, nodejs24, python314). See https://docs.cloud.google.com/run/docs/configuring/services/runtime-base-images for options. The runtime picked should match the local environment. - args: (Optional) Arguments for the command. - env: (Optional) Environment variables (e.g., name: `PYTHONPATH`, value: `./vendor`). - ports: (Optional) Container ports to expose (defaults to 8080).

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• get_brand_report

  Peec AI MCP Server

  Get a report on brand visibility, sentiment, and position across AI search engines. Results are aggregated for the entire date range by default. Use the "date" dimension for daily breakdowns. Returns columnar JSON: {columns, rows, rowCount, total}. Each row is an array of values matching column order. Columns: - brand_id — the brand ID - brand_name — the brand name - visibility: 0–1 ratio — fraction of AI responses that mention this brand. 0.45 means 45% of conversations. - mention_count: number of times the brand was mentioned - share_of_voice: 0–1 ratio — brand's fraction of total mentions across all tracked brands - sentiment: 0–100 scale — how positively AI platforms describe the brand (most brands score 65–85) - position: average ranking when the brand appears (lower is better, 1 = mentioned first) - Raw aggregation fields (for custom calculations): visibility_count, visibility_total, sentiment_sum, sentiment_count, position_sum, position_count When dimensions are selected, rows also include the relevant dimension columns: prompt_id, model_id, model_channel_id, tag_id, topic_id, chat_id, date, country_code. Dimensions explained: - prompt_id: individual search queries/prompts - model_id: AI search engine (e.g. chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, deepseek-v4-pro, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4, qwen-3-6-plus, amazon-rufus-scraper) — deprecated, prefer model_channel_id - model_channel_id: stable engine channel (e.g. openai-0, openai-1, qwen-0, openai-2, perplexity-0, perplexity-1, google-0, google-1, google-2, google-3, anthropic-0, anthropic-1, deepseek-0, meta-0, xai-0, xai-1, microsoft-0, amazon-0) — survives model upgrades - tag_id: custom user-defined tags - topic_id: topic groupings - date: (YYYY-MM-DD format) - country_code: country (ISO 3166-1 alpha-2, e.g. "US", "DE") - chat_id: individual AI chat/conversation ID Filters use {field, operator, values} where operator is "in" or "not_in". Filterable fields: model_id (deprecated), model_channel_id, tag_id, topic_id, prompt_id, brand_id, country_code, chat_id. Sort results with order_by: array of {field, direction} entries. Direction defaults to desc. Sortable fields: visibility, visibility_count, mention_count, sentiment, position, share_of_voice. Multiple entries create a multi-key sort.

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• answer_query

  mcp

  Use answer_query to get a grounded answer to a query about Google developer products. This tool has limited quota. This tool will synthesize information from the corpus to generate an answer to the query. answer_query grounds answers using the same corpus as search_documents. If you get a 429 out of quota error, use search_documents instead.

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