"PlatformView in Flutter" matching MCP tools:

• deep_research

  Data Gov In

  ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1353 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,139 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri (record-level pipeworx:// when the source emits one, else source-level). "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

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• transcribe_translate

  Sats4AI - Bitcoin-Powered AI Tools

  Compound endpoint — one payment turns audio in any of 13 source languages into both a transcript AND a translation in any of 119 target languages. Perfect for WhatsApp voice messages in a language you don't speak (Yoruba → English), or recording a meeting in another language and reading it in yours. Auto-detects source if omitted. Async — returns requestId, poll with check_job_status(jobType='transcribe-translate'). Flat price covers STT + translation. Cheaper than calling transcribe_audio + translate_text separately for typical voice messages. Pay with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='transcribe_translate'.

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• deep_research

  Ifsc In

  ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1353 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,139 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri (record-level pipeworx:// when the source emits one, else source-level). "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

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• publish_linkedin_post

  LinkedIn MCP Server (Salesbot)

  Create a LinkedIn post on behalf of a connected profile. By default the post is saved as a 'draft' in the LinkedIn Posts page so the user can review/edit it before publishing. Set auto_publish=true to publish immediately — that path still respects the user's MCP human-in-the-loop setting (when approval is required, the post stays as a draft and the user must publish it from the LinkedIn Posts page in the app). A random 30–180 s anti-detection delay is applied before the publish call. Attachments are not supported via MCP — add images in the in-app post editor.

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• clerk.revoke_sign_in_token

  Vee3

  Revoke a sign-in token in a connected Clerk application so it can no longer be used. **Sensitive** — invalidates a high-privilege token. Call clerk.get_connected_accounts first. Pass clerk_instance_id to target a specific connection, or omit it to use the default account. Returns the revoked sign-in token object. Cost = 5 tokens.

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• bulk_update_user_preferences

  Courier

  Additively create or update a user's preferences for one or more topics in a single request. Only the topics in the body are touched; existing overrides for other topics are left untouched. Partial-success: valid topics are written and returned in `items`, unapplicable ones collected in `errors`.

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• estimate_borrowing_power

  mortgage-tools

  Estimate indicative borrowing capacity, assessed at a buffered rate (APRA +3% serviceability buffer). A rough proxy for guidance — a broker models real capacity across 30+ lenders. Not credit assistance. Args: annual_income: Gross annual household income in AUD. monthly_expenses: Average monthly living expenses in AUD. existing_monthly_debt: Existing monthly loan/card commitments in AUD. interest_rate_pct: Assumed product interest rate (buffer is added on top). term_years: Loan term in years. deposit: Cash deposit available in AUD (added to the purchase budget).

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• resolve-library-id

  xpay✦ DevTools Collection

  Resolves a package/product name to a Context7-compatible library ID and returns matching libraries. You MUST call this function before 'query-docs' to obtain a valid Context7-compatible library ID UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query. Selection Process: 1. Analyze the query to understand what library/package the user is looking for 2. Return the most relevant match based on: - Name similarity to the query (exact matches prioritized) - Description relevance to the query's intent - Documentation coverage (prioritize libraries with higher Code Snippet counts) - Source reputation (consider libraries with High or Medium reputation more authoritative) - Benchmark Score: Quality indicator (100 is the highest score) Response Format: - Return the selected library ID in a clearly marked section - Provide a brief explanation for why this library was chosen - If multiple good matches exist, acknowledge this but proceed with the most relevant one - If no good matches exist, clearly state this and suggest query refinements For ambiguous queries, request clarification before proceeding with a best-guess match. IMPORTANT: Do not call this tool more than 3 times per question. If you cannot find what you need after 3 calls, use the best result you have.

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• orbital_data_centers

  dynamic-feed

  DATA CENTERS IN SPACE — curated registry of compute/AI spacecraft in orbit (Starcloud's NVIDIA H100 GPU, ESA Φsat-2 AI edge, D-Orbit in-orbit cloud), each enriched with LIVE orbital data (altitude, period, inclination) and the speed-of-light round-trip latency floor for ground links. Use for "what data centers / compute are in space, and the latency to reach them". Unique data. Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.

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• get_upgrade_link

  FundingLandscape

  Get Stripe checkout URL for subscription. IMPORTANT: Requires sign-in first. If not authenticated, returns sign-in instructions. After OAuth sign-in, call again for checkout URL. Does not count toward your monthly searches.

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• check_prompt_batch

  Nullcone Threat Intelligence

  Check multiple prompts for PROMPT IOC patterns in a single call. More efficient than calling check_prompt() in a loop — tokenization overhead is amortized and the cache reference is shared. Args: texts: List of prompt strings to check Returns: One result dict per input text, in the same order.

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• prompts_list

  DialogBrain

  List all prompt templates in this workspace. Returns id + name + description + category so you know which prompt_id to use in prompts.get or prompts.update.

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• parent_calendar_subscribe_url

  Lodi Kids Activities

  Returns the parent's personal iCal feed URL — subscribe in Google/Apple/Outlook Calendar to see practices + games in your normal calendar app. Refreshes hourly.

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• board-foot-calculator

  Microapp

  Compute board feet for lumber. Formula: (thickness_in × width_in × length_in × qty) / 144. One board foot is a piece 1 in thick × 12 in wide × 12 in long.

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• box_get_user

  Box

  Get the signed-in Box (cloud storage) user's profile: id, name, login email, total storage space (space_amount in bytes), and used storage (space_used in bytes). Use to identify the connected account or report storage usage.

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• create_project

  Plate

  Create a new project in a workspace. If a project with the same name already exists in the workspace, returns the existing project instead of creating a duplicate — check the `created` field in the response to tell the two cases apart.

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• get_email

  mailopoly

  Read a single email in full: subject, sender, recipients, date, the complete message text, attachments (each with its extracted text content when available — read these for invoice/proposal/PDF details that aren't in the body), and (optionally) actionable links found in it (pay, log in, book, track…). Accepts ids from search_emails, get_feed AND deep_search_emails — provider-history ids (the 'gmail:<id>:<id>' form) are fetched live from the mail provider.

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• art-intel__list_departments

  TO1 Intelligence Fleet

  [art-intel] List all 17 departments in the Met collection: from Egyptian Art to Modern Art to Musical Instruments. Use the department ID in search_artwork to filter results.

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• register_watch

  deals

  Register a standing intent (deal watch). The intent enters the sourcing pipeline and delivery channels are wired in the same call: a token-gated Atom feed (token returned once in this response), an optional webhook (activates after interpretation, ~1-2 min), and an optional email channel (double-opt-in). Intents with a concrete model_number auto-promote in ~60s; family-level intents require operator review (hours). specs accepts must-have attribute constraints in the webhook-filter vocabulary — unknown keys are rejected, and deals missing a constrained spec are excluded from delivery. Progress and live matches are readable via check_watch with the returned watch_id.

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• wavelength_freq

  calc.engineer

  Convert between radio frequency and wavelength. Provide either frequency in MHz or wavelength in metres, and get the full set of equivalent values: frequency in MHz and GHz, wavelength in metres, centimetres, millimetres, and feet. Essential for antenna dimensioning, waveguide selection, and quick band identification. The fundamental relationship is lambda = c / f where c is the speed of light (299 792 458 m/s).

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