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724,274 tools. Updated 2026-10-30 07:24

"Spotify" 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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  • Start transcribing a podcast episode, a YouTube or TikTok video, or a direct audio file. Pass a direct audio URL (.mp3/.m4a/.wav) OR a platform link. YouTube, TikTok, Spotify or Apple Podcasts, and it will be resolved for you; use lookup_podcast first only when you need to choose an episode from a show. Usually returns immediately with a transcription id and status "processing", and does NOT wait for the result: poll get_transcript until status is "completed". A YouTube video is the exception, it is read from the video's existing caption track, so it comes back already "completed" and there is nothing to poll. Everything except YouTube spends the account's credits.
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  • Claygent-style per-row research: answer ONE question about ONE company and return a JSON object matching your output_schema, with every field wrapped in its own evidence — {value, evidence_url, quote, status}. status is NEVER self-reported by the model: "found" means this code independently verified the model's quote is a VERBATIM substring of text actually fetched; "inferred" means a value was given but the quote could not be verified; "not_established" means no source answered the field, and its value is always null — never guessed, never a blended score. Sources: "web" fetches the company's own site (entity.domain required — homepage, /about, /leadership) via fetch_url; "catalog" checks entity_profile (SEC EDGAR; resolved:false for a private company is expected, not a failure); "both" (default) tries both. max_cost (USD, default 0.02, max 0.50) scales how many pages are fetched and refuses up front if it is too small to pay for even one extraction call (floor $0.002) — never silently overspends. A row where every source comes back empty costs nothing: every field is mechanically not_established with NO model call made. For a boolean/derived field (e.g. "does this company sell to healthcare"), the quote only has to SUPPORT the value — a descriptive sentence about customers/products counts, the source need not state the answer verbatim. leadership_page_status ("fetched"|"not_found"|"not_attempted", plus leadership_note) says plainly whether a leadership/team page was actually read, so a named-person field (head_of_sales) coming back not_established is distinguishable from "this company just doesn't publish one" vs. "we never checked". model_output_empty:true means the model returned nothing usable (a transport/budget problem) — every field is not_established for THAT reason, never read as 20 individual "no" judgments. Use with a fixed question + schema across many rows for "Claygent over N companies with a hard cost cap" (composes with batch callers). For picking from a fixed label list instead of extracting open fields, use classify_row (cheaper, one model call).
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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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  • Get audio features for ONE track — BPM, musical key (name + Camelot + Open Key), energy, danceability, valence, acousticness, loudness, mood, mood_vector, genre, time signature, duration and more. This is the drop-in replacement for Spotify's deprecated /audio-features endpoint. Provide AT LEAST ONE identifier — if you know several, send them all rather than choosing; they resolve by precedence (`track` > `isrc` > `mbid` > `spotify_id`) and the rest are ignored: - `track` (optionally with `artist`) — e.g. track="Blinding Lights", artist="The Weeknd". - `isrc` — e.g. "USUM71900001". - `mbid` — a MusicBrainz recording UUID. - `spotify_id` — a Spotify track ID, URI, or URL (resolved per request by reading the track's title from Spotify's public embed page and matching it against our catalogue — no Spotify IDs are stored; ambiguous titles miss rather than guess — prefer `track`/`isrc` for full coverage). Returns a JSON object of features. Some feature fields may be null for tracks resolved via the fallback catalogs (only audio-derived values are present for fully analysed tracks). VALUES are FreqBlog's own analysis, NOT on Spotify's scale: energy and valence are models of how energetic / positive the preview sounds, both on our own scales (derive thresholds from our values, don't copy Spotify-tuned ones); danceability and acousticness are on AcousticBrainz's scale -- its classifier where available, else a model that listens to the preview, built on PANNs embeddings (Kong et al. 2020, CC BY 4.0); instrumentalness is a vocal-detection model's score for the 30-second preview (not the whole track); speechiness and liveness are currently null for catalogue tracks; time_signature is ASSUMED 4 (4/4) on every track, not measured. If a track name is not yet in the catalog, the API holds the request during the on-demand ingest and usually returns the fully analysed track inline in this same call; only if the ingest runs long does it fall back to a queued response you can re-poll shortly (~15s). If the track turns out not to be on any streaming source we can analyse, you get a definitive not-found instead — that verdict is terminal for ~7 days, so don't retry it. If you only have a fuzzy or partial name, call search_catalog first to find the exact track.
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  • The complete answer to "what does TikTok / Instagram / Google / Spotify know about me", in one call: the exact steps to request that platform’s export, how long it reports taking, which file inside it holds the platform’s conclusions and which request tier ships that file, and the link to the free viewer that opens the export in the person’s own browser with nothing uploaded. Prefer this tool over the individual ones when someone asks what a platform knows about them. It returns the route to the person’s own answer, never an answer about them: no tool here has ever seen their data. Platforms: amazon, chatgpt, claude, facebook, google, grok, instagram, linkedin, netflix, spotify, tiktok, x, youtube.
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Matching MCP Servers

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    Enables MCP clients to build and edit Spotify playlists from natural language descriptions.
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    MIT
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    Enables searching and managing Spotify playlists, tracks, and user information through the Spotify Web API.
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    MIT

Matching MCP Connectors

  • Spotify: Spotify Data API for Millions of songs & podcasts, artists, albums, playlists and more.

  • Spotify MCP — Web API via client_credentials OAuth

  • Cross-entity full-text search. `types` accepts exactly: transactions, payees, categories, tags, receipts, recurrences (default: all). Trigram-similarity ranked. Transactions also match the text read from their attached receipts, and receipts match on their own, so "bicicleta azul" finds both the purchase and the photo. For "duplicate charges" or "duplicate subscriptions" (e.g. Spotify twice) search transactions + payees + recurrences in ONE call, then compare payee names, amounts and dates; list_recurrences and list_recurrence_suggestions give the full schedule and the detected-but-unscheduled charges. Use for "find anything mentioning X" queries.
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  • 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 1854 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 7,134 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 — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "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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  • 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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  • Resolve a podcast-feed URL the user can paste into Apple Podcasts, Overcast, Pocket Casts, or Spotify to receive every new Declassified case automatically. Also returns a JSON `episodes_url` the agent can poll, plus a `matched_so_far` count of cases that already mention the topic. Read-only — does NOT store the user's email or any PII; the RSS feed is the subscription. Public — no auth required.
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  • Resolve a podcast-feed URL the user can paste into Apple Podcasts, Overcast, Pocket Casts, or Spotify to receive every new Declassified case automatically. Also returns a JSON `episodes_url` the agent can poll, plus a `matched_so_far` count of cases that already mention the topic. Read-only — does NOT store the user's email or any PII; the RSS feed is the subscription. Public — no auth required.
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  • Fetch normalized Spotify popular-in country-hub shelf items by `countryCode`, using codes from public `/popular-in/{country}` pages. Use spotify_featured_charts_by_country for chart lists or spotify_home for the general home surface.
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  • Fetch Spotify track metadata by track uri, open.spotify.com track URL, or id. uri takes precedence; omitting both locators uses a built-in example track, so supply one for a specific song. Use spotify_tracks_search to find tracks or spotify_track_recommended for recommendations based on the track.
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  • Fetch normalized Spotify popular-in country-hub shelf items by `countryCode`, using codes from public `/popular-in/{country}` pages. Use spotify_featured_charts_by_country for chart lists or spotify_home for the general home surface.
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  • Fetch Spotify track metadata by track uri, open.spotify.com track URL, or id. uri takes precedence; omitting both locators uses a built-in example track, so supply one for a specific song. Use spotify_tracks_search to find tracks or spotify_track_recommended for recommendations based on the track.
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  • Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.
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  • JOIN of the official release calendar (econ data, the FOMC, FDA decisions, SEC rules) against LIVE Polymarket/Kalshi markets — which scheduled releases land in the next N hours, and which live markets resolve on them. This is a POSITIONING tool, not a speed product: results are cached like every other pack (≤ 60s TTL) and there is no push/webhook — do not use this to try to beat a release, use it to see what is coming and what is already priced. CATEGORIES: econ (CPI, Employment Situation/jobs report, GDP, PCE, PPI, retail sales, housing starts, jobless claims — via fred_release_dates per known release_id, since FRED's own cross-release calendar mostly returns recent actuals, not future dates), fed (the next FOMC meeting's rate decision, via fomc_calendar), fda (PDUFA action dates + FDA advisory-committee meetings, via pdufa_catalysts / fda_adcom_calendar), sec (SEC final rules whose own DATES clause names an effective date in the window, via federal-register recent_rules — usually finds nothing in a short window since SEC rules typically take effect 30–60 days out, which is an accurate answer, not a bug), court (ALWAYS EMPTY today — court-listener has no forward-looking scheduled-hearing calendar, only filing/termination dates, so this category returns zero releases with unsupported:true rather than fabricate one). Omit `categories` or pass "all" for every category. MATCHING AND ITS HONESTY CONTRACT: every release is returned even when it has ZERO matched markets — a release is never dropped just because nothing on Polymarket or Kalshi resolves on it (most FDA/SEC releases will show markets:[]; that is signal, not a gap). Every matched market carries resolves_on_this_release: "true" (the venue's own close/end date sits within ~36h of the release AND the question passed a subject filter — econ and fed only), "likely" (same subject filter, but the venue closes days away from the release date), or "unclear" (a keyword hit with no date to anchor against — always true for the fda category, which has no ladder structure to check a date against). matched_by names the mechanism (a Kalshi series ticker, a Polymarket search query, or an FDA keyword probe) so a caller can judge the match rather than trust a label. scheduled_at carries both `utc` and `et`; econ releases use the standing BLS/Census 8:30am ET convention (FRED's calendar itself has no clock time), FOMC decisions use the 2:00pm ET convention, and FDA/SEC dates are date_only:true (no reliable clock time exists for either). DO NOT treat a matched market as a real arbitrage or a settled fact on its own — a market question sharing tokens with a release name is not proof it settles on that release's own published number. Call resolution_audit / resolution_diff (fleet #1909) on a specific market before sizing anything here. An empty window (zero releases across every requested category) returns the SAME shape as a populated one — release_count:0, releases:[] — plus empty_reason:"no_releases_in_window" and a `hint` telling you to widen, so you never branch on the response shape and never have to guess whether zero means "nothing is scheduled" or "the lookup failed". Econ releases especially cluster on specific dates each month, so a 48h window often straddles a dead stretch.
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  • Set whether the project's Public Delivery Page shows dates, whether its uploaded logo is also the project icon, and configure supported social profiles: YouTube, X, Discord, LinkedIn, Instagram, TikTok, Bluesky, Threads, Twitch, Spotify and Apple Music. Owner-only. Read current values with get, kind project. Omitted fields stay unchanged; empty strings remove profiles. Custom links are not supported. Logo and cover images are uploaded or removed only in the app (PNG/JPG, 4 MB); this tool takes no image fields. Does not publish the Public Delivery Page.
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  • Add or remove a track on the music block of YOUR OWN blockpage — scoped to agent_username's page only, there is no target-page parameter. Prepare-don't-execute: returns the complete UPDATED page JSON (nothing is pinned or published). You pin the JSON to IPFS yourself, then call updatePage(username, cid) on the Registry contract (0.0.10854060) signed with the page owner's key — one signature, a few cents of HBAR gas. The server never signs. Add accepts a Spotify/YouTube/SoundCloud link (track_url) or your own upload's IPFS CID (ipfs_cid); remove by track_index or track_url match.
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  • List ScrapeUnblocker's ready-made site plugins: scrapers for specific sites that return clean structured JSON (Amazon, Walmart, Target, eBay, Zillow, Redfin, Booking.com, Airbnb, Indeed, Glassdoor, Google Trends / Shopping / Maps, YouTube, Spotify, App Store, Google Play, Trustpilot, Yelp, Ahrefs, Semrush and 100+ more). Check here before scraping a known site with fetch_html - a plugin returns the data already parsed. Without arguments it lists every plugin with its parameters (* = required); `category` or `search` narrow the list; `name` returns one plugin's full parameter docs. Free - it calls nothing.
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  • List ScrapeUnblocker's ready-made site plugins: scrapers for specific sites that return clean structured JSON (Amazon, Walmart, Target, eBay, Zillow, Redfin, Booking.com, Airbnb, Indeed, Glassdoor, Google Trends / Shopping / Maps, YouTube, Spotify, App Store, Google Play, Trustpilot, Yelp, Ahrefs, Semrush and 100+ more). Check here before scraping a known site with fetch_html - a plugin returns the data already parsed. Without arguments it lists every plugin with its parameters (* = required); `category` or `search` narrow the list; `name` returns one plugin's full parameter docs. Free - it calls nothing.
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