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650,446 tools. Updated 2026-10-13 08:58

"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. IMPORTANT, HOW TO READ THE RESPONSE OF THIS ENDPOINT, WHICH SPENDS CREDITS. Queries listed in `guidesFailed` are PROVEN not to have produced a guide and their credit was given back (unless the account has unlimited credits, where nothing was reserved): re-sending them is free and correct. Queries listed in `guidesUnknown` have an UNDECIDABLE outcome and their credit is deliberately KEPT, because the guide was most likely written: DO NOT re-send them, you would pay for the same guide twice. Look them up in `GET /api/v1/guides` after a few minutes instead, and contact support if nothing shows up. Finally, a `200` is NOT a promise that every query produced a guide: compare `guides.length` with the number of queries you sent, never read `success` alone, and never re-send a query just because it is missing from `guides`.
    ConnectorNo auth
  • Which export destinations this account has connected (Close, GoHighLevel, Google Sheets) and whether each is ready to receive leads. Check this BEFORE export_leads or create_automation with a CRM destination — those fail if the integration isn't connected, and Close additionally needs one manual export first to save its field mapping. File destinations (csv/xlsx/json) always work and need nothing connected.
    ConnectorNo auth
  • Archive a CRM company (status inactive). Safer than delete — keeps counterparty and history. PATCH /api/crm/companies/:companyId. Scope write:crm; RBAC crm.leads.edit.
    Connector
    Destructive
    OAuth
  • Permanently delete a CRM company. Clears company links on leads/activities; does not delete the counterparty. Prefer crm_archive_company. DELETE /api/crm/companies/:companyId. Scope write:crm; RBAC crm.leads.edit.
    Connector
    Destructive
    OAuth
  • Stage a move of a client's card to another step on a pipeline board they are already on (buyer, seller, lead or tenant), as dragging the card on the CRM board does. Returns a preview and an action_id; NOTHING changes until you call confirm_action with that id, after the agent has said yes. Use the stage name as it reads on the board (my_pipeline lists them). Putting a client on a board they are not on yet, or qualifying a lead, is qualify_lead. Won and Lost need details the CRM asks for, so they are done in the CRM. For an offer stage, pass offer_amount (and property_reference if the card has no listing yet). Requires a linked PropertyList account: the change is made in the agent's own name, with their CRM permissions, so an agency API key alone cannot make it.
    ConnectorNo auth
  • List supported Google Maps place type values for search filters. Returns place_types as a string array. Use a value with place_type on google-maps.search or google-maps.nearby_search. Cost = 1 token.
    ConnectorNo auth

Matching MCP Servers

  • A
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    面向 Codex、WorkBuddy 等 AI 工具的知你AI助手合并版 Skills,涵盖当前会话分诊、客户查询与画像、历史会话检索、客户分群和 API Key 用量查询,并连接个微、企微、视频号、微信小程序、公众号、服务号、微信客服、微信小店、抖音号、小红书、微博、网站及H5客服等渠道。
    MIT No Attribution
  • A
    license
    A
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    Enables AI apps to work with Google Sheets in plain language: finding spreadsheets, reading and writing ranges, managing sheets, formatting, validation, protected ranges, tables, charts, and access control, with deliberate writes and destructive operations clearly marked.
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    73 npm
    1
    MIT

Matching MCP Connectors

  • Read the text content of a Google Doc, Sheet, or Slides presentation. Docs export as plain text, Sheets as CSV, Slides as plain text. For large files, returns a size warning instead of content — use max_chars and offset to read in chunks. file_id also accepts a ShareWatch handoff code of the form SW-XXXXXXXX. The user gets one by opening a file from Google Drive with ShareWatch, and it stands for that file until it goes unused for 30 days. The result always reports the real file_id, so use that value for any follow-up call rather than the code.
    ConnectorOAuth
  • Publish a multi-scenario comparison into the signed-in user's Google Sheets — sim_compare's export, the counterpart of sim_publish for a single scenario. Runs every scenario on one shared seed (the same server-enforced sharing sim_compare uses, so differences are the scenarios and not the dice) and writes a comparison table plus a trajectory chart, rather than one scenario's own data tabs. Returns the sheet URL. Counts against the same daily publish quota as sim_publish, and costs 1 token per scenario from your account's compute bucket.
    Connector
    Destructive
    OAuth
  • Take contacts OUT of a named lead list after reviewing them (e.g. with get_contact_profile / get_lead_context you decide a lead does not belong). Because every contact must live in exactly one list, this MOVES them back to the default 'CRM Imports' bucket rather than deleting anything — the contact, its CRM history and any campaign membership are preserved, so the action is fully reversible with add_contacts_to_list. This does NOT delete the contact and does NOT remove it from campaigns (use exclude_contacts_from_campaign for that). To permanently drop a lead from all future outreach, blacklist it instead. Returns how many were removed; contacts already in 'CRM Imports' are reported as not_removed.
    ConnectorAPI key
  • List where your signals are delivered — Slack channels, webhooks, Google Sheets, n8n, Clay and Notion — with each destination’s health and recent failure state. Connection settings (URLs, tokens) are never returned.
    ConnectorOAuth
  • Read one Drive file's TEXT content by id. Google-native docs (Docs/Sheets/Slides) are exported as text (default text/plain, spreadsheets text/csv — override with export_mime_type); other files are read as UTF-8 text directly (garbles binary formats like images/PDFs — use download_file_content for those). Content is capped (large files are truncated, `truncated: true`). Needs the Drive CONTENT scope (drive.readonly) — accounts without it get a reconnect hint. Pass `account` when more than one content-capable account is connected.
    ConnectorOAuth
  • Download one Drive file's raw bytes as base64 (binary-safe — images, PDFs, etc.). NOT for Google-native Docs/Sheets/Slides (no raw binary exists — use read_file_content with export_mime_type instead). Hard-capped at 5MB; larger files error rather than silently truncating. Needs the Drive CONTENT scope (drive.readonly). Pass `account` when more than one content-capable account is connected.
    ConnectorOAuth
  • Read one Google Drive file by id (from drive_search) as text: a Google Doc or Slides as plain text, a Google Sheet as CSV (its first sheet), a text, CSV, JSON, Markdown or XML file as it is. Other files (PDF, images, Office) return their details and link only. Long files are cut; say so when you quote them.
    ConnectorNo auth
  • Read one Google Drive file by id (from drive_search) as text: a Google Doc or Slides as plain text, a Google Sheet as CSV (its first sheet), a text, CSV, JSON, Markdown or XML file as it is. Other files (PDF, images, Office) return their details and link only. Long files are cut; say so when you quote them.
    ConnectorNo auth
  • 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.
    ConnectorOAuth
  • Find outliers and anomalies in structured data — ideal as a second step after pulling records from Google Sheets, Airtable, Supabase, Notion databases, HubSpot, Financial APIs, GitHub, NPM, or any source that returns rows of JSON. Fully stateless: send known-good rows as training and suspect rows as test in ONE call. Returns per-row anomaly scores, confidence levels, and the top features explaining WHY each row was flagged. Typical workflow: (1) Pull data from another tool (e.g. Google Sheets, Supabase query, HubSpot deals). (2) Pass the first N rows as training (normal baseline). (3) Pass remaining or new rows as test. (4) Report which rows are anomalous and why. Works on JSON objects, numbers, text, arrays. No separate training step required. Examples: - Spreadsheet QA: Pull 500 sales rows from Sheets → train on first 400 → test last 100 → flag outlier entries - Financial screening: Get ratios for 50 stocks from a financial API → find anomalous ones - CRM hygiene: Pull HubSpot deals → flag deals with unusual discount/value patterns - Dependency audit: Get NPM package metrics → flag packages with anomalous quality scores - Commit review: Pull GitHub commit metadata → flag unusual commit patterns
    ConnectorNo auth
  • Use only for custom CRM fields. For stage use set_deal_stage; for contact details use update_contact. Set custom field values on a lead (by contact_id). Pass `fields` as an object of { field_key: value }. Only defined field keys are accepted (see list_crm_fields). Adds the lead to the CRM if needed.
    ConnectorAPI key
  • Find outliers and anomalies in structured data — ideal as a second step after pulling records from Google Sheets, Airtable, Supabase, Notion databases, HubSpot, Financial APIs, GitHub, NPM, or any source that returns rows of JSON. Fully stateless: send known-good rows as training and suspect rows as test in ONE call. Returns per-row anomaly scores, confidence levels, and the top features explaining WHY each row was flagged. Typical workflow: (1) Pull data from another tool (e.g. Google Sheets, Supabase query, HubSpot deals). (2) Pass the first N rows as training (normal baseline). (3) Pass remaining or new rows as test. (4) Report which rows are anomalous and why. Works on JSON objects, numbers, text, arrays. No separate training step required. Examples: - Spreadsheet QA: Pull 500 sales rows from Sheets → train on first 400 → test last 100 → flag outlier entries - Financial screening: Get ratios for 50 stocks from a financial API → find anomalous ones - CRM hygiene: Pull HubSpot deals → flag deals with unusual discount/value patterns - Dependency audit: Get NPM package metrics → flag packages with anomalous quality scores - Commit review: Pull GitHub commit metadata → flag unusual commit patterns
    ConnectorNo auth
  • Upload one or more offline conversions (e.g. a booked call from your CRM) to the ad platform, closing the click-id attribution loop so the platform can credit the originating ad click. Pass body as a single conversion or { conversions: [ { gclid | msclkid, conversionAction (the conversion-action name, e.g. "Booked call"), occurredAt (ISO 8601), value?, currency?, orderId? } ] }. A gclid routes to Google Ads, an msclkid to Microsoft Advertising. Idempotent on orderId — re-uploading the same orderId is a no-op. Scoped to the active Space — see set_active_space to switch, or pass space_id to override for this one call.
    ConnectorAPI key
  • Draft adding or removing services, using ids from my_service_options, plus custom services Google does not list. Every service not named stays on the listing. Creates a DRAFT only — nothing on Google changes. Show the owner the draft this returns, word for word, and publish it with publish_change only after they say yes.
    ConnectorNo auth
  • Get the STANDARD published Wilderness Times checklist as a ready-made PDF — the full, unedited list, free and with no signup. Available for camping, backpacking, winter camping and day hiking. IMPORTANT: this is the standard list and does NOT reflect any customisation. If the user has added, removed or filtered items, call share_checklist instead — the link it returns opens their own list in the checklist builder, which exports to PDF, Excel, Google Sheets or print with the gear links intact.
    ConnectorNo auth