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687,020 tools. Updated 2026-10-22 09:09

"Gmail" matching MCP tools:

  • Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FEES: every opportunities[] row and partition_check.arbitrage carry edge_pp_gross (== gap_pp / overround_pp), fees_pp, edge_pp_net, net_positive, plus polymarket_fee_pp, fee_basis and fee_categories[]. BOTH cost components are modeled: Polymarket's own per-category TAKER FEE (fee = shares × rate × p × (1-p), rates crypto 0.07 / sports-economics-culture-weather-other 0.05 / finance-politics-mentions-tech 0.04, geopolitics and world events fee-free; verified against Polymarket's own docs as of 2026-09-13) and Polygon gas (~$0.02/leg). The taker fee dominates: ~$1.75 per 100 shares on a crypto market at 50c versus $0.02 of gas, so rows that looked profitable before fleet #1927 may now show net_positive:false — that is the correction, not a regression. Each leg is priced at ITS OWN market's rate and price (the fee curve peaks at 50c and falls toward both extremes). fee_basis says where the rate came from: 'payload' (read off the market, the normal case), 'category' (mapped from its fee category), 'fee_free', or 'fallback' (rate unknown — charged at the modal 0.05 rather than assumed free, so an unreadable market is never reported as costless). Where fill_check reprices against live depth, this does NOT double-count that spread cost. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.
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  • Extract the settlement clause of a single Polymarket or Kalshi market: who publishes the settling number (source), the clock time + timezone it is taken at, the precision of the computation (e.g. "1-minute candle close" vs "60-second trailing average" vs "election outcome"), the evidence standard (official_source | consensus_reporting | any_credible_report | unspecified), and void_handling (cancellation/postponement settlement — reused verbatim from bet_research's cancellation_rule detector, not re-derived). Parses Polymarket's `description` field (fetched via polymarket_market) or Kalshi's `rules_primary` + `rules_secondary` fields (fetched via kalshi_market) with regex + a small vocabulary — no LLM pass, so an unusual clause reports confidence:"low" rather than a guess. Pass `market` as a Polymarket slug/URL or a Kalshi market ticker (e.g. "KXBTCD-26SEP1317-T66999.99"); a Kalshi EVENT ticker (e.g. "KXBTCD-26SEP1317") also works — it picks one representative market under that event, since the settlement mechanism is normally shared across all strikes/legs in one event. Use this before treating a polymarket_kalshi_spread row as a real arbitrage: two ladders that look alike can settle on different sources, at different times, with different precision — this tool is how you check. Pair with resolution_diff to compare two markets directly. KNOWN GAP: idiosyncratic phrasing that doesn't match the vocabulary returns confidence:"low" and evidence_standard:"unspecified" rather than an LLM-guessed answer.
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  • Open one attachment of a Gmail message and return what is in it. Text, CSV, JSON, HTML and code files come back as themselves; images come back as pictures the assistant can look at. PDFs, spreadsheets and Word files can't be read yet — but any attachment can be saved as-is with save_gmail_attachment_to_drive. Attachments up to 10 MB. Get attachmentId from get_gmail_message or get_gmail_thread.
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  • Create a Gmail DRAFT (saved, not sent) — new mail, or a reply when thread_id is set (from a search result / get_thread). A thread reply is drafted in the account that received the thread, as Reply All (original sender + original To/Cc) with proper threading headers, unless reply_mode='sender_only'; the result reports the account, to and cc. Unlike send_message this never delivers anything, so no pre-send confirmation is needed; the user reviews and sends it from Gmail. Needs gmail.compose — accounts connected before this feature get a reconnect hint. Pass `account` when more than one Gmail account is connected.
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  • Replace the signature on the mailbox's own send-as identity. Nothing of the old one is kept, so read it first with settings_send_as_get. An empty signature clears it. Gmail mailboxes only: an Outlook mailbox exposes no signature to write.
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  • Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.
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Matching MCP Servers

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    Self-hosted, multi-account Gmail MCP server for Claude and Codex over Streamable HTTP, with explicit account aliases, owner-only OAuth, and encrypted credential storage. Supports searching and reading email, creating drafts, and optional sending and label changes.
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    Enables sending email from verified Gmail sendAs aliases and downloading inbound attachment data as files, filling gaps in the stock Gmail MCP.
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Matching MCP Connectors

  • Gmail MCP Pack

  • gmailOAuth

    A MCP server for Gmail that lets you search, read, and draft emails and replies.

  • Move, copy, flag or archive messages you name by message_id, in one inbox. Get message ids from email_read first. Every action acts only on the ids you pass and is undone by another call: a move by a move back, archive by a move into the Inbox, flag by the opposite flag, and a copy leaves the original untouched. Copy follows the CONNECTOR, not the address: inbox_list reports it per inbox as capabilities.copy, true for every IMAP inbox (a Gmail address connected over IMAP included) and for Outlook, false only on the Gmail API connector, which has no copy operation at all. On Gmail a move adds the destination label and removes INBOX, leaving other labels in place; moving a message OUT of Trash or Spam into a real label also clears TRASH/SPAM, so it is a genuine restore rather than a labelled message still queued for deletion. To move everything matching a search instead of a list of ids, use email_search_and_move, which is its own tool because a wrong filter there relocates a whole inbox. Needs manage:folders; deleting is the separate email_delete tool.
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  • Search one of the authenticated user's connected Gmail inboxes (read-only). Reuses Integrations hub Gmail (service_connections provider=gmail). Optional account/email selects a mailbox when multiple are connected — call list_gmail_accounts first. Prefer Gmail operators: from:, subject:, is:unread, newer_than:7d, has:attachment. For clock-time constraints, pass after/before as account-local civil datetimes; the tool converts them to precise UTC instants. Returns id, subject, from, canonical date, account-local date_local, time_zone, and snippet only (no body), newest first (newest_first: true). For the latest / most recent email from someone, query from:<sender> alone, without quoted topic phrases (the newest reply often lacks them); a from: + topic search with no match from the last 7 days also searches the sender alone and marks those messages sender_only (see note). Follow up with read_gmail_message. Cannot send, archive, or delete. Never invent email contents. If Gmail is not connected, connect at /integrations. Requires mail.read on an API key or non-ChatGPT OAuth connection. ChatGPT profiles do not grant mail.read or list this tool. Share tokens cannot call this tool. ($0.10; API key required)
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  • Search the user's connected Gmail mailboxes and return matching messages, newest first: from, to, subject, date, and a short snippet of the body. `query` uses Gmail search syntax: 'in:sent to:jane@acme.com' for messages the user sent to someone, 'from:jane@acme.com' for messages they received, plus operators like subject:, newer_than:7d, and has:attachment. So 'show me the last 5 messages I sent to jane@acme.com' is query 'in:sent to:jane@acme.com' with max_results 5. Each result carries a `starred` flag; a message the user starred matters to them, so weight it accordingly; 'is:starred' finds starred mail directly. For 'how many' questions, use total_matches_estimate in the result (it is Gmail's estimate of ALL matches, beyond the messages returned). If it reports no Gmail account connected, give the user the link it returns. Rare header-only connections cannot run query search; the tool says so. Answer correspondence questions from the brain for those.
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  • Send one email to one contact, in their conversation thread — by conversation_id, or contact_id to start or continue that contact's thread. It goes out from this account's sender: ShearQuery's own address for the ShearQuery team (replies come back into the thread), or the member's own connected Gmail for everyone else (replies go to their Gmail inbox). If no sender is set up, it says where to connect Gmail. A reply can leave out subject (it reuses the thread's, with Re:). Read the exact message back to the owner and get their OK first: it can't be unsent. Refused for a contact with no email or marked do-not-contact. channel is email; texting isn't connected yet.
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  • Use this when the user asks whether mail from a domain will reach Gmail or Outlook, or why it is rejected or goes to spam, such as "will Gmail accept mail from example.com sent from 203.0.113.7?". Pass the domain, the sending IPv4 address if known, the return-path domain (mail_from) if known and, optionally, up to 5 DKIM selectors. Returns one verdict (likely_accepted, at_risk, likely_rejected or incomplete) with a verdict per provider, the top 3 causes each with one fix, the exact SPF and DMARC records to paste (records_to_paste) and the DKIM steps for the detected mail provider (fix_steps), every rule checked with its official source link (SPF for the IP, DKIM, DMARC, reverse DNS, blocklists), the 6 blocklists with their terms links, what was not checked, and the source and as_of date. Gmail and Outlook are always both reported, so there is no provider argument. It reads public DNS and free blocklists only: it sends no test message and cannot see sender reputation, so it does not promise inbox placement.
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  • Search the caller's own connected Gmail, now, for one supplier's mail in a date window: the sender name or domain, from `from` to `to` inclusive, sent mail excluded. Use it for a missing receipt or invoice, one call per supplier, with the window widened by about a week on each side of the payment date because a receipt often arrives before or after the charge. It returns up to `max_results` messages, newest first, each with `from`, `subject`, `sent_at`, a short `snippet`, `already_in_well` and an `open_url` to the message in Gmail. Compare the snippet and subject against the gap's amount and date yourself: a message from the supplier is a find only when it fits that charge. `already_in_well: true` means Well's mail sync already turned that message into documents, so the receipt can be matched in Well rather than fetched. It only reads: it attaches nothing, imports nothing and contacts no one, so it needs no confirmation. `mailbox` is the address of the Gmail account searched, so an answer can name it. `status`: `searched` (the search ran; empty `matches` is a real "not in the mailbox"), `no_mailbox` (the caller has no connected Gmail in this workspace; another member's mailbox is never searched), `needs_reconnect` (the Gmail grant expired: offer to reconnect Gmail), `unavailable` (Gmail did not answer: say the mailbox could not be searched, never "not found"). When the token authorizes one workspace, call this directly — no workspace call is needed first. When it authorizes several, this read will not guess which one you mean: pass `workspace_id` on the call.
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  • Follow up on applications that have gone quiet. action "list" shows the applications whose follow-up is due, most urgent first; "history" shows the follow-ups already sent; "draft" writes a follow-up for one application_id and spends tokens; "send" emails it to the address in to, from the user's connected Gmail or Outlook, under the user's name, and cannot be undone; "record_sent" records a follow-up the user sent themselves, for example on LinkedIn. Send only on the user's explicit go-ahead: show the exact subject and text first, then send it only after they say yes to that text. Sending needs a connected Gmail or Outlook; without one, the user sends it themselves and you record it with record_sent. On a timeout or an error from send, call action history before sending again. Do not use it to answer an email an employer sent; use aiapplyd_reply_to_email. Next: action draft for an application from the list.
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  • Start connecting Gmail, Shopify, Zendesk, Front or Gorgias. Returns a dashboard `url` that opens the sign-in for that service, and `tellUser`: give the user both, then check get_setup_status once they are done. Pass Zendesk `subdomain`, Gorgias `account` or Shopify `shop` when known; otherwise the page asks. Helpdesks (Zendesk, Front, Gorgias) need the Pro plan (`plan_feature_unavailable` otherwise); set_up_helpdesk finishes one once connected.
    ConnectorOAuth
  • "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).
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  • TYPED, DETERMINISTIC financial facts for a US public company for an EXPLICITLY NAMED reporting period — "Apple revenue for fiscal 2023", "Walmart net income FY2026 Q3", "Microsoft cash at the end of fiscal 2024". PREFER OVER entity_profile / get_company_financials whenever the period matters: those answer "the most recent figures" and will happily hand back FY2025 when you asked about FY2019, and neither separates a discrete quarter from a year-to-date figure. This one refuses instead — it NEVER substitutes the latest period for the period requested, NEVER returns 0 for missing data, NEVER lets a 9-month YTD number answer a quarterly question, and NEVER converts a currency. Every answer carries the exact us-gaap concept it came from, what that concept MEASURES (NetIncomeLoss excludes non-controlling interests, ProfitLoss includes them — not synonyms), the accession number and a link to the filing on sec.gov, the restatement trail of any superseded figures, and a contract + derivation version to pin against. Fiscal periods are the FILER'S OWN, anchored on their fiscal-year end, so Walmart's year ending 2026-01-31 is FY2026 and Apple's ending 2025-09-27 is FY2025. Attributes: revenue, net_income, cash, gross_profit, operating_income, eps_diluted, capex, operating_cash_flow, long_term_debt, shares_outstanding (the dei COVER-PAGE share count, so its period end is the cover date, after the quarter closed — that is the most current count the filing carries). Units are not all USD: shares_outstanding is in `shares` with currency null, eps_diluted in `USD/shares`. A discrete quarter the filer never filed — every Q4, and Q2/Q3 of cash-flow lines, which are filed year-to-date — is DERIVED as YTD(q) minus YTD(q-1) and comes back with `reported: false`, the subtraction named in `derivation` (fy_minus_9m / ytd_9m_minus_6m / ytd_6m_minus_3m) and both legs with their filings in `derived_from`; test `reported` if you will only accept a figure lifted from a filing. Per-share and balance-sheet figures are NEVER derived. Every non-answer is a named status — `unavailable` (the filer did not report it for that period; the periods that DO exist are listed, without values), `unsupported` (outside what v1 covers — a non-us-gaap filer, an unknown attribute, a non-USD unit), `ambiguous` (the company name matched two filers equally well; both are named), `conflicting` (two filings the same day disagree; both are returned and neither is picked), `partial` (a value with no accession behind it). Source: SEC EDGAR, XBRL financial facts, one publisher read once — see `corroboration`. Same response is served at POST https://gateway.pipeworx.io/v1/facts for non-MCP callers.
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  • Search the NATIONAL BUSINESS REGISTERS by company name and get the register's own id back — a Companies House number, a Norwegian organisasjonsnummer, a Finnish business id, a SIREN, a Japanese corporate number, an Estonian registry code, a CNPJ, or an LEI. Use this when you want the OFFICIAL REGISTERED ENTITY and the country is not the US, or when you do not know which country it is. Registers searched: Companies House (GB), Enhetsregisteret (NO), PRH/YTJ (FI), Sirene (FR), 法人番号公表サイト (JP), RIK e-Business Register (EE), and the GLEIF LEI crosswalk, which reaches jurisdictions we have no register pack for (DE, SE, AU, SG...). THIS TOOL REFUSES TO GUESS, and that is its point: when two different companies match a name equally well it answers `match_status: "ambiguous"` with BOTH, rather than naming one. A one-word trading name usually IS ambiguous — "Bolt", "Apple", "Next", "Sea", "DNB" each match a well-known group AND unrelated micro-entities whose registered name is exactly that word — so pass `country` when you know it. Every response names EVERY register it asked under `searched[]`, with that register's own as-of date and row count, and a register that could not be reached carries an `error` there rather than reading as zero rows: "we could not ask Companies House" is never the same answer as "this company is not in Britain". `related[]` holds entities that collapsed INTO the match — the same company seen through another register, or another country's arm sharing its LEI — so a multinational returns one answer rather than tying with itself. NOT A PEOPLE SEARCH: sole traders and other natural persons are excluded on the publisher's own legal-form field, never guessed from the shape of a name. For US public companies prefer resolve_entity (CIK + ticker + FIGI); the two share this same matcher, and resolve_entity returns these register fields too.
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  • What did a web page say on a given date? Finds the archived capture of a URL nearest that date across the Internet Archive Wayback Machine, Common Crawl and Arquivo.pt, reads it, and returns the readable text (HTML stripped) with the capture timestamp, a link to the archived copy, and the GAP between the date asked for and the capture actually used (e.g. "Captured 1.5 day(s) before the requested date"). One call replaces the find-capture, fetch-record, parse-HTML chain. If no archive holds a capture within max_gap_days (default 30) it answers status "no_capture" and names the nearest capture before and after the date with their gaps, so a distant capture is never passed off as the page on that date. Every archive's outcome is listed in sources[]; one that timed out or errored says so with its upstream status, and if all of them failed the answer is error "archives_unavailable", not no_capture. Captures are immutable and cached by capture id; served_from says whether the text came from cache or upstream. Use archive_compare to see what changed between two dates.
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  • List all Gmail labels for the authenticated user. Returns both system labels (INBOX, SENT, TRASH, etc.) and user-created labels with message/thread counts. Use this to discover label IDs needed for add_labels, remove_labels, or search_email queries.
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  • Add and/or remove the same labels on many email messages at once in a single Gmail API call (wraps users.messages.batchModify). Use this for bulk operations such as marking multiple specific emails as read (remove ['UNREAD']), archiving (remove ['INBOX']), or starring (add ['STARRED']). Use list_labels to find label IDs (Gmail requires IDs, not names). Common system label IDs: INBOX, STARRED, IMPORTANT, UNREAD, SPAM, TRASH. Provide 1-1000 email IDs and at least one of `add` or `remove`. The same label changes are applied to every listed message. Gmail returns no per-message status, so a successful call means Gmail accepted the request; an invalid email ID or label ID typically causes Gmail to reject it.
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  • Create, rename and delete mailbox folders, which are labels on Gmail: the arguments say 'folder' for cross-provider compatibility, but Gmail manages labels (type: 'label'). Deleting is irreversible, and on Gmail it strips the label from every message carrying it. Every action needs manage:folders. Use folder_list to read the folders that exist and to get the folder_id these actions take.
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