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444,007 tools. Updated 2026-08-11 15:31

"Searching for Google Sheets MCP (Minimum Cell Production or similar)" matching MCP tools:

  • Add, rename or delete a tab in a Google Spreadsheet. action:"add" + title · action:"rename" + tab + newTitle · action:"delete" + tab. Name the tab by its TITLE or its numeric sheetId (list_sheet_tabs gives both); an unknown tab is refused with the real list rather than a Google error nobody can map back. DELETING a tab destroys everything on it: call it without confirm first to get the filled-cell count, then confirm:true + confirmCells. Google does not allow removing the LAST remaining tab in a file, and that is refused by name with the way out (clear it, or delete the whole file with delete_drive_file). Every action is read back from the spreadsheet before it is reported as done.
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  • Say what you want in one typed object and get the answer, without choosing a primitive. `type` is a tagged union: it selects the intent AND decides which other fields are read, so send only the fields its row needs. The plan is EXECUTED in the same call, so you receive the result (the resolved cell64, the similarity, the delta, the verdict), not a list of calls to make yourself. type | needs | optional | answers where_is | description | | cell64 for a named place what_is_here | cell OR place | description | what is attested at a location is_like | a, b | | cosine similarity of two cells did_change | cell, band, window | | delta for one band over [start,end] tslots find_like | key | k, filter | nearest cells by embedding confirm | claim, cell | | verdict plus the signed facts behind it ask | description | place/cell/lat+lng | free-text question, packaged answer An unknown or missing `type` returns a structured `needs_intent_type` envelope naming the seven values rather than a hard error, so you can correct it on the next turn. When to use: Call when the user's question maps cleanly onto one of the seven rows above and you would rather state the goal than pick a primitive. Reach past it for anything else: a specific band at a cell is emem_recall, a region is emem_recall_polygon, and a free-text place question with no obvious primitive is emem_ask directly (type:"ask" here just forwards to it). `window` takes tslots, not dates: get valid ones from emem_trajectory first. Example arguments: {"type":"did_change","cell":"damO.zb000.xUti.zde78","band":"indices.ndvi","window":[20245,20620]}
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  • Read-only queries on the open spreadsheet. No data is modified. Safe to auto-approve. Call as {"action": "<name>", "params": {...}} — per-action params are listed in the Action Reference below. Special actions (not shown in the action enum): • batch — {"action": "batch", "params": {"actions": [{"action": "<name>", "params": {...}}, ...]}}. Runs reads in parallel; individual failures are reported per-entry without short-circuiting. • context — {"action": "context", "params": {"topic": "<name>"}} or {"action": "context", "params": {"action": "<name>"}}. Returns deeper docs for a topic or a single action's signature. Plural "topics" / "actions" arrays are also accepted and may be combined. Topics: python, javascript, formula, connection, validation, a1, quadratic, chart, pivot_table. Action Reference • get_cell_data(selection, page?, sheet_name?) — Returns cell values for a selection in A1 notation. Supports comma-separated ranges to fetch multiple areas in ONE call, including across different sheets. Examples: "A1:B10, D1:E10", "TableName, OtherTable", "'Sheet1'!A1:B10, 'Sheet2'!C1:D10". Table names are globally unique so they work without sheet prefixes. For cell ranges on other sheets use 'SheetName'!Range. Only use when you need the full dataset (aggregations, lookups, analysis). The file summary already includes sample rows. Results may be paginated — use page (0-based) for additional pages. • has_cell_data(selection, sheet_name?) — Check if any cells in a selection have data. Returns true if ANY cell contains data. Use before creating/moving tables or code to avoid spill errors. All ranges MUST be on the same sheet. • get_code_cell_value(code_cell_position?, code_cell_name?, sheet_name?) — Get full code from an existing Python, JavaScript, or connection code cell. Do NOT use for formula cells — formulas are already in get_cell_data results and the file summary. • get_text_formats(selection, page?, sheet_name?) — Get text formatting info. Use table column references for tables ("Table_Name[Column Name]"). Results may be paginated. • get_validations(sheet_name?) — Get all validations in a sheet. • get_conditional_formats(sheet_name) — Get all conditional formatting rules. Use to check existing rules before creating/updating/deleting. • text_search(query, case_sensitive?, whole_cell?, search_code?, regex?, sheet_name?) — Search for text in cell outputs. Supports regex when enabled (e.g., "\d+", "^hello", "foo|bar"). Searches cell outputs only, not code. Booleans default false. • get_sheet_info() — List all sheets and names. • get_spreadsheet_context(sheet_name?, include_errors?) — Full context snapshot of the file. • read_data(selection, sheet_name?, max_rows?) — Read cell data as compact CSV. Auto-tiers: returns all rows for small/medium data (<5000 rows), head+tail preview for large data. Preferred over get_cell_data for most reads. • outline(sheet_name?) — Structural map of the file: sheets, bounds, tables, code cells, charts, connections, errors. Use to understand file layout before reading data. • dependencies(position, sheet_name?, direction?) — Trace cell dependencies. direction: "forward" (what this cell reads), "reverse" (what depends on this cell), or "both" (default). • list_connections(team_uuid?) — List all database connections in a team (PostgreSQL, MySQL, MS SQL, Snowflake, BigQuery, Mixpanel, Google Analytics, Plaid, etc.). Returns each connection's uuid, name, and type. team_uuid is optional — if omitted, the user's only team is used; multi-team users must pass it. Call this BEFORE get_database_schemas or set_sql_code_cell_value to discover the connection_ids and connection types you need. • get_database_schemas(connection_ids, connection_type, team_uuid) — Get table/column schemas for database connections. Always call before writing SQL. Get connection_ids from list_connections. connection_type: POSTGRES, MYSQL, MSSQL, SNOWFLAKE, BIGQUERY, COCKROACHDB, etc. • list_agent_connections(team_uuid?) — List the team's ready Agent Connections (third-party REST API bindings). Returns each connection's uuid, name, service, base URL, auth pattern, and `{{SECRET_NAME}}` references to use in fetch code. team_uuid is optional — if omitted, the user's only team is used; multi-team users must pass it. Reference secrets via `{{SECRET_NAME}}` in Python/JavaScript fetch code; the connection proxy substitutes team secret values at request time. • inspect_agent_connection(connection_id, team_uuid?) — Get the full schema (resources, endpoints, fields, docs URLs) and plan for one ready Agent Connection by uuid (from list_agent_connections). Call BEFORE writing fetch code against a connection so you don't guess at endpoints. team_uuid is optional with the same single-team fallback as list_agent_connections. Batch: • batch(actions) — actions: [{action, params}]. Runs reads in parallel through this same tool; per-entry failures are reported in the result without short-circuiting the batch. `action` may be any name from this reference. Nested `context` items are allowed and returned alongside the reads.
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  • Resolve a city or airport name or code before searching flights. Returns typed values such as city:SHA to search every catalog airport in Shanghai or airport:SHA for Hongqiao only. Pass the selected value unchanged to search_flights. Ask the traveler when multiple results are plausible.
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  • 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.
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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 — one connection covers Drive, Sheets and Docs). NOTE: Hermoso uses the drive.file scope, so it reaches ONLY the files it created plus any the user explicitly handed over with the Google file picker in the app — never their whole Drive.
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Matching MCP Servers

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  • Query your Google Sheets as structured JSON: list sheets and tabs, read schemas, filter rows.

  • Streamable HTTP MCP server for Google Calendar and Sheets with OAuth login.

  • Find similar or competitor websites based on classification. Takes a URL, classifies it (or uses cached classification), and returns other websites from the same category and subcategory. Useful for competitive analysis and discovering related content. Rate limited to 1 request per minute per domain. Args: url: The website URL to find similar sites for. limit: Maximum number of similar sites to return (1-50, default 10). Returns: Dictionary with: - url: The input URL (normalized) - classification: The URL's category and subcategory - similar_sites: List of similar URLs from the same category - total_in_category: Total sites in this category/subcategory - cached: Whether the classification was from cache
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  • Upload a photo to one of your Stay's photo areas. Supply EXACTLY ONE of 'url' (a public https:// link, e.g. a Google Drive or Dropbox share link — the server downloads it) or 'base64' (the raw image bytes, base64-encoded — use this when you already have the image data in hand, e.g. a user attached a photo in the conversation, and have nowhere public to host it first). The photo is validated against the same minimum specs as a manual upload: JPEG, PNG, or WebP, under 20MB, and a minimum resolution that depends on area. Most areas require at least 1920x1080 landscape; 'host' requires at least 1080x1350 PORTRAIT — a landscape photo will be rejected for that area. Every upload is downscaled to fit within 2560x1440 and re-encoded server-side as WebP (stripping metadata and anything that isn't genuine image data) before storage. Requires NOMADSTAYS_MCP_AGENT_TOKEN.
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  • 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.
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  • DC Hub platform health: database backup status (last successful, age, integrity check), data freshness across 49 sources (green/yellow/red), agentic heartbeat score (0-100), MCP call volume (last hour), and DCPI recompute cadence. Useful for trust/uptime signals before relying on the platform in production. Try: get_backup_status. Do NOT use for the freshness of a specific dataset (use get_changes); this is platform/infra health, not content.
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  • Add an output cell to a session that's already reached CP3. Use this when the user picked a small initial cell set, previewed the drafts, and now wants another surface (e.g. the session started with linkedin:text_post and the user wants to add instagram:carousel too). Text-only cells (linkedin:text_post, x:thread, long_form_article, etc.): generates text via the matching generator if it hasn't run yet, then creates the Output row. This call blocks synchronously ~20-30s when it must run a new generator family (no status to poll); it returns fast when that family already generated. Idempotent: if the cell is already on the session, returns the existing Output unchanged. Asset cells (linkedin:image_post, x:reel, instagram:image_post, etc.): creates the text Output row; image-bearing cells auto-attach a free branded card (static_urls populated without an explicit render, swappable anytime), while video/reel assets are never auto-rendered. The response's next_step says exactly what to call for more: niche_render_image_card (cell plus an explicit background: 'photo' or 'brand_color') or niche_render_reel (cell). The response's copy_lineage says whether this cell drafted fresh text or shares its generator family's existing copy. Remove: pass `remove_cell` to delete a produced cell the user no longer wants on this run. Idempotent: a clean message when the cell isn't present. Errors: 400 if session is pre-CP3; 422 if cell is invalid.
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  • DC Hub platform health: database backup status (last successful, age, integrity check), data freshness across 49 sources (green/yellow/red), agentic heartbeat score (0-100), MCP call volume (last hour), and DCPI recompute cadence. Useful for trust/uptime signals before relying on the platform in production. Try: get_backup_status. Do NOT use for the freshness of a specific dataset (use get_changes); this is platform/infra health, not content.
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  • DEV ONLY — Sign and broadcast an unsigned transaction using a local private key (PK env var). For production, use a dedicated wallet MCP server (Fireblocks, Safe, Turnkey, etc.) instead of this tool. Takes the transaction object returned by any write.* tool and submits it onchain.
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  • Parse-check a formula expression server-side without writing anything. Returns { ok, error?, rewrittenFormula?, referencedFunctions, unknownFunctions }. Use BEFORE update_row / create_row when the formula references functions or syntax you're not 100% sure of: a `=SUMIFS(...)` with the wrong arg order or a misspelled `=AVERAG(...)` will round-trip into the cell as a stored carrier with no value, and the user will see #NAME? or #VALUE? on next view. Catch it here. `unknownFunctions` flags any identifier that isn't in the Dock Sheets catalog (including likely typos); `referencedFunctions` lists the canonical post-alias names the engine will see. Cheap, public, no auth, no workspace context needed.
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  • Generate a read-only probability forecast for a clearly stated future event by searching relevant prediction markets and synthesizing evidence. Use when the user asks for a probability, outlook, or forecast; use polybridge_search when they only need market discovery. Does not place trades, provide financial advice, or access private/internal data.
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  • Call this when a shortlisted provider is too expensive, unreachable or a poor fit: substitutes for one known provider — same niche first, topped up by similar capability — each with observed price, endpoint liveness, community upvotes and how_to_connect (website, docs, mcp endpoint) so a substitute is immediately usable. Set cheaper_only to shop down from the subject's price. Accepts `agent_id` (aliases: handle, id). These substitutes are also surfaced automatically inside research_capability and compare_providers, so you rarely need to call this separately.
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  • Find cancer cell lines by name, ranked by mutation burden. Returns each matching cell line with its tissue of origin, cancer type, total mutations, unique mutant peptides, data sources (COSMIC / DepMap-CCLE / PubMed), and Cellosaurus ID. Covers only cell-line models (kept separate from primary tissue samples). Use before `get_cell_line` or `top_genes_in_cell_line`. Ordered by mutation count (most mutated first).
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  • Score one L2 order-book snapshot through the sealed single-layer path and return a scalar entropy plus engine attestation. Purpose: Score one snapshot end-to-end through the sealed engine and surface the result plus the engine sha that produced it. Use when: You are validating Kirk on your own data before committing to a production path, or you are scoring a single snapshot inside an interactive workflow (rate-limited at 60 req/min per account). Do not use when: You need throughput above interactive scale, or you are in a per-book loop from an LLM. MCP round-trip is millisecond-scale and inappropriate for latency-critical work. For >200 books, call kirk_bulk_howto first — the returned stdlib Python client scores at zero LLM tokens per iteration. Capability class(es): - C2 (variable-universe cross-section entropy scoring — same model handles any N without retraining). - C5 (sealed engine sha stamped on every response). - C6 (bit-exact reproducibility across substrates; validated by the FY24 252-day reproduction, byte-identical on repeat runs). Path fit: Validation via MCP (this tool). Production integrations run in-process under sealed-engine attestation — same binary sha as this endpoint. Contact Kavara for deployment options. MCP is a validation and discovery surface, not a latency-critical production path. Cost: 1 IU per call. LLM tokens accrue on top for agent-driven callers.
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  • Score up to 500 L2 order-book snapshots in one MCP call — returns an entropies list plus engine attestation. Purpose: Batch-score up to 500 snapshots through the sealed engine in a single MCP dispatch. Use when: You are validating batch behaviour, comparing entropy distributions across small book sets, or running interactive experiments up to 500 books at a time. Do not use when: You have more than 500 books, or you are looping this tool from an LLM. Batches >500 raise a structured `batch_too_large` before any ledger debit. For sustained bulk work, call kirk_bulk_howto — the stdlib Python client scores at zero LLM tokens per iteration. Capability class(es): - C2 (variable-universe cross-section entropy — heterogeneous batch shapes are handled by one model without retraining). - C5 (sealed engine sha stamped on every response). - C6 (bit-exact reproducibility across substrates and runs). Path fit: Validation via MCP (this tool). Production bulk workloads run in-process under sealed-engine attestation — same binary sha as this endpoint. Contact Kavara for deployment options. The MCP round-trip is inappropriate for high-throughput consumption. Cost: 1 IU per 50 books (minimum 1 IU per call). n≤50 → 1 IU; n=51..100 → 2 IU; a full 500-book batch → 10 IU. Validation tier — validation-scale limits. LLM-agent-scoped cap at 500 books; use kirk_bulk_howto for anything larger.
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