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306,510 tools. Last updated 2026-07-25 11:18

"Using Google Sheets as a CRM" matching MCP tools:

  • Create new guides Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional `source` field (default `google`). How to request each guide type: 1. Google SERP guide (1 credit per guide): omit `source`, or pass `source: "google"`. Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: `source: "google_ai_overview"` builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; `source: "google_ai_overview_citations"` builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"} 2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. `source: "chatgpt"`. The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"} 3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the `_citations` suffix, e.g. `source: "chatgpt_citations"`. The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"} Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer `<engine>_citations`: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (`<engine>` alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick `<engine>_citations`. The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query.
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  • Save a Hermoso render — or ANY file — into the user’s connected Google Drive. Pass a Hermoso render URL as url (or urls[] for several); for a local/external file, call upload_file first and pass the url it returns. Optional folder (created if new) + name. Returns the Drive file(s) with a webViewLink. Needs Google Drive connected (Settings ▸ Connectors ▸ Google Drive). NOTE: Hermoso uses the drive.file scope, so it can only see/manage files IT created in the user’s Drive — not their whole Drive.
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  • List all Google Trends category and subcategory labels you can pass to other Google Trends tools in the category field. Returns cat (array of category names, including All categories) and msg. Use this before interest-over-time or interest-by-region calls when filtering by category. Cost = 5 tokens.
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  • Search worldwide patents by keyword, inventor, assignee, or phrase using Google Patents. Returns patent id, title, assignee, inventor, filing/publication dates, and a snippet. Args: query: Free-text query (e.g. "quantum error correction", "lithium battery anode"). max_results: Maximum number of patents to return (1-30, default 10).
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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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  • Extract tables and forms as Markdown from a PDF or image (base64-encoded). Use when the document contains structured tabular data such as financial statements, data sheets, or forms. For plain prose documents, use extract_text instead. Returns: { pages: number, text: string } — text contains Markdown-formatted tables. Example prompts: - "Extract the tables from this financial statement." - "Pull the data table from this PDF into Markdown format." - "Get the tabular data from this form document."
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Matching MCP Servers

Matching MCP Connectors

  • 斯特丹STERDAN天猫旗舰店产品咨询MCP Server。洛阳30年源头工厂,高端钢制办公家具,1374个SKU,涵盖保密柜、更衣柜、公寓床、货架、快递柜。BIFMA认证,出口35+国家。8个工具:产品目录查询、场景推荐、认证资质、采购政策、维护指南等。

  • Google Shopping products, prices, sellers, and deals as structured data via a hosted MCP server.

  • List all countries and subregions you can pass to other Google Trends tools in the country and region fields. Returns geo.countries: each country name maps to country (label) and regions (array of subregion names). Also returns msg. Use this before interest-over-time or interest-by-region calls when filtering by geography. Pair with google-trends.categories when filtering by category. Cost = 5 tokens.
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  • Fetch Google Trends related queries for one to five keywords. Returns a JSON object whose top-level keys are your keywords. Each value has top and rising sections; each section has query (rank index to query string) and value (rank index to score). Requires start in datetime-with-timezone form (for example 2020-05-01T00:43:37+0100). Optional end defaults to now. country defaults to global; region requires a valid country. category and gprop default to all when omitted or empty. Use google-trends.categories and google-trends.regions to discover valid category, country, and region values. Cost = 40 tokens.
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  • Query marketing data and analyze any website — analytics, SEO, advertising, e-commerce, CRM, social media, site health & brand identity, competitive intelligence, content creation, and data visualization. Always use a single call, even when the question spans multiple data sources or channels (e.g., GA4 + Google Search Console + Google Ads + CRM). The server auto-routes internally to all needed sources and returns a combined response with the same depth and granularity as individual queries — do NOT split multi-source or multi-channel questions into separate calls.
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  • Load Lenny Zeltser's complete cybersecurity-writing rating toolkit: all 7 sheets, scoring policy, scoring playbook, and cross-references to the writing guidelines. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • Create a CRM account/customer with a primary contact. Optionally enroll the account as a member; use enrol_membership later when the account already exists.
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  • Store a generated outreach message on a CRM lead so it becomes durable context — e.g. an email, an email follow-up, a LinkedIn message or LI follow-up. The CRM is a 'sponge': you save the copy here, then read it back later (get_lead_context / list_lead_messages) and push it to the right channel via that channel's own tool/MCP (e.g. Smartlead for email). Does NOT send anything. Pass message_id to update an existing draft instead of creating a new one.
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  • Save a verified bank account as the organization's payout destination. Verify the account first using neuron_verify_bank_account.
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  • Return the guide for BULK-importing an existing folder of documents (Word docs, spreadsheets, PDFs, notes) into a space as a page tree — rather than hand-creating pages one at a time. Read this FIRST for any 'import', 'migrate my docs', 'bring in this folder', or 'load these files' request. Covers converting Office files locally (docx → pages, spreadsheets → live sheets), attaching PDFs/images, and the import endpoint's dry-run → confirm → commit contract.
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  • Push leads into a connected destination: 'close' (CRM — uses the account's saved field mapping), 'ghl' (GoHighLevel sub-account), 'google_sheets' (new spreadsheet, or pass spreadsheet_id for one LeadMarina created before), or a file — 'csv' | 'xlsx' | 'json' (emailed to the account owner + download link). Exports EITHER one search's leads (pass search_id) or the whole library (set all=true, capped at 1000, optionally narrowed with the same filters as query_leads). Previously exported leads are UPDATED in place (matched by Google CID) — never duplicated; the user's own CRM notes/columns are never touched.
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  • 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. This tool returns the generated answer_text and a list of document names (references) used to generate the answer. Use get_documents with the document names to fetch the entire document content if needed. If you get a 429 out of quota error, use search_documents instead.
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  • "Who owns AS[N]" / "AS[number] info" / "what company is ASN [X]" / "Cloudflare / Google / Amazon ASN" — summary for an Autonomous System Number (ASN): holder organization, country, AS type (transit / content / IXP), allocation date. Pass "AS15169" or "15169". Use for network attribution, BGP analysis.
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  • Given a need (e.g. 'outbound', 'CRM', 'automation'), return StackSwap's recommended affiliate partner(s) with sign-up URL and positioning.
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  • Extract tables and forms as Markdown from a PDF or image (base64-encoded). Use when the document contains structured tabular data such as financial statements, data sheets, or forms. For plain prose documents, use extract_text instead. Returns: { pages: number, text: string } — text contains Markdown-formatted tables. Example prompts: - "Extract the tables from this financial statement." - "Pull the data table from this PDF into Markdown format." - "Get the tabular data from this form document."
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  • List supported language codes for Google Maps place endpoints. Returns languages as a map of language names to codes (for example English: en). Use these codes with the language parameter on place detail, review, and photo calls. Cost = 1 token.
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