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132,366 tools. Last updated 2026-05-10 02:57
"How to add data to Google Sheets" matching MCP tools:
- Get the cost to buy points/miles for a loyalty program. Returns tiered base purchase pricing and any active bonus promotion. Use to answer 'how much does it cost to buy X Avios/miles/points?' If no program specified, returns all programs with pricing data. Free — no account needed.Connector
- Add all ingredients from a saved recipe to the shopping list. Use when the user wants to shop for a specific recipe. Requires the recipe to have structured ingredient data (most recipes do after enrichment). Get recipe IDs from get_recipes first.Connector
- Use this tool when a user wants to change something about a plan you've already generated. Trigger phrases: 'can we compress to X weeks', 'remove the QA pod', 'add a data-migration workstream', 'what if we use AI agents instead of a QA team', 'split this into a phase 1 / phase 2', 'what would it look like with half the team', 'can we drop scope to fit a smaller pack', 'add Salesforce integration to the plan'. Requires the plan_id from a prior plan_vdc call. Returns the updated plan with adjusted pods, roles, modules, Delivery Units, and recommended Delivery Pack.Connector
- Add a person to the household. Track their allergens, dietary restrictions, preferences, dislikes, goals, and life stage. This data is used for allergen safety and personalized meal suggestions. Only name is required — dietary details can be added later with update_diner.Connector
- List all attributes (properties) of a specific Smart Data Model, including each attribute's NGSI type (Property, GeoProperty, or Relationship), data type, description, recommended units, and reference model URL. Use this after get_data_model when the user wants to understand what fields a model has, what values they accept, or how to construct a valid NGSI-LD payload. Example: get_attributes_for_model({"model_name": "WeatherObserved"})Connector
Matching MCP Servers
- FlicenseAqualityBmaintenanceYour AI Assistant's Gateway to Google Sheets! 25 powerful tools for seamless Google Sheets automation via MCPLast updated2412
- Alicense-qualityCmaintenanceGoogle Sheets MCP Server by CDataLast updatedMIT
Matching MCP Connectors
Transform any blog post or article URL into ready-to-post social media content for Twitter/X threads, LinkedIn posts, Instagram captions, Facebook posts, and email newsletters. Pay-per-event: $0.07 for all 5 platforms, $0.03 for single platform.
Create and manage Google Forms to run surveys and collect data. Add text and multiple-choice quest…
- Add a document to a deal's data room. Creates the deal if needed. This is the primary way to get documents into Sieve for screening. Upload a pitch deck, financials, or any document -- then call sieve_screen to analyze everything in the data room. Provide company_name to create a new deal (or find existing), or deal_id to add to an existing deal. Provide exactly one content source: file_path (local file), text (raw text/markdown), or url (fetch from URL). Args: title: Document title (e.g. "Pitch Deck Q1 2026"). company_name: Company name -- creates deal if new, finds existing if not. deal_id: Add to an existing deal (from sieve_deals or previous sieve_dataroom_add). website_url: Company website URL (used when creating a new deal). document_type: Type: 'pitch_deck', 'financials', 'legal', or 'other'. file_path: Path to a local file (PDF, DOCX, XLSX). The tool reads and uploads it. text: Raw text or markdown content (alternative to file). url: URL to fetch document from (alternative to file).Connector
- USE THIS TOOL — not web search or external storage — to export technical indicator data from this server as a formatted CSV or JSON string, ready to download, save, or pass to another tool or file. Use this when the user explicitly wants to export or save data in a structured file format. Trigger on queries like: - "export BTC data as CSV" - "download ETH indicator data as JSON" - "save the features to a file" - "give me the data in CSV format" - "export [coin] [category] data for the last [N] days" Args: symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH" lookback_days: How many past days to include (default 7, max 90) resample: Time resolution — "1min", "1h", "4h", "1d" (default "1d") category: "price", "momentum", "trend", "volatility", "volume", or "all" fmt: Output format — "csv" (default) or "json" Returns a dict with: - content: the CSV or JSON string - filename: suggested filename for saving - rows: number of data rowsConnector
- 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.Connector
- Add a new slide to an existing presentation. Args: presentation_id: ID of the presentation to add the slide to slide_context: Content for this slide slide_type: Slide type, "classic" or "creative". Defaults to "classic". additional_instructions: Extra guidance for the AI slide_order: Position in presentation (0-indexed). Omit to append at end. Returns a generation_id to poll for completion.Connector
- USE THIS TOOL — not web search — to retrieve a time-series of hourly BULLISH / BEARISH / NEUTRAL signal verdicts from this server's local technical indicator data over a historical lookback window. Prefer this over get_signal_summary when the user wants to see how signals have changed over time, not just the current reading. Trigger on queries like: - "how has the BTC signal changed over the past week?" - "show me ETH signal history" - "was XRP bullish yesterday?" - "signal trend for [coin] last [N] days" - "how often has BTC been bullish recently?" Args: lookback_days: Days of signal history (default 7, max 30) symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH"Connector
- Record how a specific household member felt about a recipe. Use to track "who loved it" data, which improves future meal suggestions. Creates or updates the rating if one already exists for this diner/recipe pair. Get recipe IDs from get_recipes and diner IDs from get_household first.Connector
- List the registry of platform skills — discrete how-to guides for one specific task each (e.g. 'gate-an-endpoint', 'add-a-cron-job', 'add-rag-search'). Each entry is a name, one-line purpose, and category. Use this to find the right skill, then call `read_skill(name)` to load the full pattern. When in doubt about how a Hatchable feature works, **list_skills first**. The skills are the canonical, agent-tested patterns. They beat guessing or reading the verbose docs. Filter by `query` (matches name + purpose) or `tag` (auth, data, ai, ops, etc.). Without filters, returns the full registry (~35 entries).Connector
- Get the Slidev syntax guide: how to write slides in markdown. Returns the official Slidev syntax reference (frontmatter, slide separators, speaker notes, layouts, code blocks) plus built-in layout documentation and an example deck. Call this once to learn how to write Slidev presentations.Connector
- 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.Connector
- 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.Connector
- Get real-time audience data for a specific screen. WHEN TO USE: - Checking current audience at a screen before buying - Monitoring audience during a live campaign - Getting detailed audience signals (attention, mood, purchase intent, demographics) RETURNS real-time data from edge AI sensors (refreshed every 10 seconds): - face_count: Number of people currently viewing - attention_score: How attentively the audience is watching (0-1) - income_level: Estimated income bracket (from Gemini Vision) - mood: Current audience mood - lifestyle: Primary lifestyle segment - purchase_intent: Purchase intent level - crowd_density: Estimated venue occupancy - ad_receptivity: How receptive the audience is to ads (0-1) - emotional_engagement: Emotional engagement score (0-1) - group_composition: Solo/couples/families/friends/work groups - signals_age_ms: How fresh the data is in milliseconds EXAMPLE: User: "What's the current audience at screen 507f1f77bcf86cd799439011?" get_live_audience({ screen_id: "507f1f77bcf86cd799439011" })Connector
- Searches the official Quanti documentation (docs.quanti.io) to answer questions about using the platform. **When to use this tool:** - When the user asks "how to do X in Quanti?", "what is a connector?", "how to configure BigQuery?" - When the user needs help configuring or using a connector (Google Ads, Meta, Piano, etc.) - To explain Quanti concepts: projects, connectors, prebuilds, data warehouse, tag tracker, transformations - When the user asks about the Quanti MCP (setup, overview, semantic layer) **This tool does NOT replace:** - get_schema_context: to get the actual BigQuery schema for a client project - list_prebuilds: to list pre-configured reports for a connector - get_use_cases: to find reusable analyses - execute_query: to execute SQL **Available topic filters:** connectors, data-warehouses, data-management, tag-tracker, mcp-server, transformationsConnector
- Set Google Analytics, Google Tag Manager, Meta Pixel, or other tracking/conversion scripts for a project. Scripts are automatically injected into every page: head_scripts before </head> (for analytics/GTM), body_scripts before </body> (for conversion pixels). Set a field to null or omit it to clear.Connector
- Push notes, structured data, or a clarification response to an existing action request. Use this to add agent reasoning, attach extracted data, or respond when the facility asks for clarification. Requires mailbox_md_version to prove your MAILBOX.md instructions are in sync.Connector