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627,899 tools. Updated 2026-10-01 19:23

"Concepts and explorations related to thinking and cognition" matching MCP tools:

  • List the shows most related to a podcast, best first — "shows like this show". Each result carries the related show's slug, a calibrated score in (0,1], and a coarse band (strong: same beat and audience; moderate: overlapping subject or audience; weak: a loose connection) to branch on. Add `include: ["basis"]` to see WHY each pair is related: content similarity of recent episodes, shared topics, shared guests (named), same publisher, shared sponsors — use it to explain a recommendation or to keep only pairs related for the reason you care about (shared guests for booking, content for media planning). Related sets are precomputed per show from its transcripts, topic profile, guest roster, network and advertisers, restricted to the show's language. Only shows above a relatedness floor are listed, machine-generated and farmed feeds are never listed, and a publisher's duplicate feeds of one show appear once. An empty FIRST page is not an error: its `coverage` says whether the set is not computed yet, nothing cleared the floor, or the request's filters and the default policy removed everything; an empty page reached through a cursor is simply the end of the list. Not a topic browser: for shows that COVER a topic use `particle_podcast_resolve` with `topic_slug`. Not a guest lookup: for where a person has appeared use `particle_podcast_get_guest`. Not advertiser co-occurrence: use `particle_podcast_get_sponsors`. Every related show's slug feeds `particle_podcast_resolve`, `particle_podcast_list_episodes` and the other podcast tools; person slugs in the basis feed `particle_podcast_get_guest`, topic slugs feed `particle_podcast_resolve`'s `topic_slug`. For the five most related shows inline on a resolve, pass `include: ["related"]` to `particle_podcast_resolve` instead of calling this tool.
    ConnectorOAuth
  • Heista's creative direction engine — same engine the Creative Director specialist runs internally, exposed over MCP. ONE-SHOT: give a brief, get N finished creative outputs. For back-and-forth refinement, or output shapes the `medium` enum below does not cover, use chat_with_creative_worlds instead. OUTPUT SHAPE switches on the `medium` arg: • omitted → N territory cards (default exploration). Each card sits on different psychology / craft / feel / world axis coordinates so the set spans the creative space rather than orbiting one insight. Card has: name, campaign line, 5-8 sentence pitch, one-sentence strategic bet, resolved axis state names, creative-director rationale. • `tvc` → N TVC scripts (15-90s — hook, arc, resolve, sound design, end line). • `billboard` / `ooh` / `print` → N out-of-home concepts (visual concept + line + placement rationale). • `social` → N social-video concepts (hook + format type + middle beat + payoff, optimised for Reels / TikTok / Shorts). • `activation` / `experiential` → N activation concepts (space design + user journey + peak moment + takeaway artifact). • `audio` → N sonic / radio concepts (sonic scene + voice + audio arc). • `campaign` → N full campaign platforms (insight → big idea → strategy → visual world → production roadmap). The engine can also produce manifesto / copy, naming, packaging, PR stunts, content series, brand positioning, partnerships — these output shapes are NOT in the medium enum, so use chat_with_creative_worlds when the user wants one of those. USE WHEN: user says "give me ideas / options / directions / territories", "what angles work for...", "show me three / five ways to...", "write a TVC for...", "draft billboard concepts for...", "I need fresh thinking on...". DO NOT USE to refine one existing direction (use chat tool), to critique work, for OKRs / internal docs / strategy decks, or anything outside advertising creative direction. INPUTS: brief (the creative problem, free text), count (2-6 concepts), optional brand_id (from list_brands or any create_powersource_* — when provided the engine grounds output in the brand's buyer tensions, voice, and selling points), optional medium (above), optional lens_hint (apply a playbook or signature move as a creative constraint), idempotency_key (safely retryable for 5 minutes). Returns the finished creative output as narrative text PLUS a structured array of resolved axis coordinates for programmatic use. Metered — typically 3-15 credits per call depending on count and brand context size. Charged after success on actual token usage.
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  • Quick company lookup: facilities (with addresses and operations) and enforcement actions (recalls) for a single company and its known aliases. Costs 1 credit. Excludes: 510(k) clearances, PMA approvals, drug applications, inspection history, and subsidiary data. Related: fda_company_full (adds clearances/approvals/drugs for 5 credits), fda_suggest_subsidiaries (discover related entities), fda_get_facility (per-facility products and operations by FEI).
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  • Deducts a unit from the customer's available workflow allowance. Do not call this tool when the customer is requesting assistance related to the pay wall and its subscriptions. This tool should be called after each AI response that is not pay wall related. @param customer_id: The customer's database id @return: a json object, containing the customer_id and remaining fup token balance in the "values" object
    ConnectorNo auth
  • Concept art for a whole game, reused before generated. Use it when a user asks for a game "like God of War", "a GTA", "a Zelda": call stage=plan with the game they named. The plan lists concepts that already exist for that game (GripForge community catalog or the workspace) and theme assets already in the library: reuse them first. It then prices the missing concepts: one style bible, heroes and bosses one image each (MOBA and hero games: each hero with its ability kit and ability icons, see hero_kits), enemies, NPCs, weapons, vehicles, buildings, props and VFX in sheets of 6 cut automatically, textures in sheets of 2 turned into seamless 1024 px textures. Show the plan and price to the user; only call stage=build with a budget after they agree. Concepts are private review images; nothing is sent to 3D. The plan also returns ambient_kits: the kits of the theme's atmosphere (weather, lighting, snow tracks) with their config; install them in the game project next to the blueprint's gameplay kits.
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Matching MCP Servers

  • A
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    quality
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    maintenance
    Enables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.
    AGPL 3.0
  • A
    license
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    quality
    C
    maintenance
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    1
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Matching MCP Connectors

  • Rick and Morty MCP — wraps the Rick and Morty API (free, no auth)

  • Find relevant Smart‑Thinking memories fast. Fetch full entries by ID to get complete context. Spee…

  • One gol24.uz news item together with all related items of the same story (every step of a transfer saga, reactions to one match), related items newest first. Use it after latest_news, club_news, person_news or search_news returned an item and the user wants the background or follow-ups. Accepts the item's id, its slug, or the full https://gol24.uz/yangilik/... URL. Returns the item and up to 20 related items with the same fields as latest_news; "related" is empty when the item has no story yet. Returns an error if the item does not exist or is not published. Rate limit: 60 requests per minute.
    ConnectorNo auth
  • Point VARRD's autonomous AI in a direction and let it discover edges for you. Give it a topic and it draws from one of the most comprehensive market structure knowledge graphs ever built — containing ideologies and theories, not statistics — so it generates genuinely novel hypotheses rather than overfitting to what already worked. BEST FOR: Exploring a space broadly. Give it 'momentum on grains' and it might test wheat seasonal patterns, corn spread reversals, or soybean crush ratio momentum. It propagates from your seed idea into related concepts you might not think of. Returns a complete result — edge or no edge, stats, trade setup. Each call tests ONE hypothesis through the full pipeline (~$0.25/idea). Call again for another idea. Use 'varrd_ai' instead when YOU have a specific idea to test and want full control over each step.
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  • Compare 2-10 named companies across 1-8 XBRL concepts, aligned on calendar periods. This is the middle shape between secedgar_get_financials (one company, one concept, full history) and secedgar_fetch_frames (one concept, one period, every reporting company) — reach for it when the question names the companies. One companyfacts read per company, resolved through the same frame dedup and tag priority as secedgar_get_financials so the numbers agree. Balance-sheet and entity-info concepts are filed as point-in-time values and align on the calendar year (annual) or quarter (quarterly) their snapshot falls in, so they sit in the same matrix as income-statement lines. The inline matrix covers the most recent periods up to `periods`, trimmed further when companies x concepts x periods is too large to return in one response; the full aligned series is materialized as df_<id> for growth rates and spreads — inspect it with secedgar_dataframe_describe, then analyze it with secedgar_dataframe_query. A company that fails to resolve is reported in failed_companies and the comparison proceeds with the rest, and a company that does not report a concept is reported in gaps with the tags that were tried — never interpolated or zero-filled. A company that reports a concept only for periods older than the inline window is named in caveats with its newest period. A concept that is neither a friendly name nor an XBRL tag is reported once in unknown_concepts with the closest supported names, and fails the call only when every concept is one. Off-calendar filers and unit mismatches are surfaced in caveats rather than silently mixed.
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  • AUTHORITATIVE full XBRL fundamentals dump for a US public company. Send the company as `cik` — that argument takes a TICKER ("NVDA") or a CIK ("320193"), and `ticker` / `ticker_or_cik` are accepted as aliases for it. Returns every reported financial metric (hundreds of concepts: revenue, net income, assets, liabilities, EPS, cash flow lines, segment breakdowns) with annual and historical values pulled straight from the company's SEC filings — the official numbers, not estimates. Use when you need the complete fundamental picture vs. one metric (for one metric use edgar_company_concept). Leads with latest_annual — revenue, net income, assets, cash, EPS for the most recent fiscal year, resolved to whichever XBRL concept the filer currently reports under — and flags retired concepts (e.g. a pre-ASC-606 Revenues tag) as stale so a 2010 figure is never mistaken for current. Large payload; agents typically use this once to discover available concepts then narrow to edgar_company_concept for follow-up queries. For just the headline figures plus the recent filings list, edgar_company_snapshot is the smaller one-call answer.
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  • List available MCP tools and get detailed help. Use this tool to discover what tools are available and how to use them. Call without parameters to see all tools, or provide a tool name to get detailed help including parameters, examples, and related tools.
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  • Returns the full relationship graph for a given Lexicon term. Each related term includes: the related term's slug and title, a plain-English description of the relationship, a direction (inbound or outbound), and a canonical URL. Read-only. No LLM calls. Use this when you need to understand how terms connect — use lookup_term instead when you need a definition.
    ConnectorNo auth
  • Read-only. Use first when the agent needs Dreamlit product guidance, prompting guidance, approved workspace context, project setup, schema hints, workflow state, or relevant app URLs. Returns a compact context pack with concepts, recommended tool flow, actor/workspace/project data, optional authoring context, optional workflow context, and appUrls. Do not use this to create, update, publish, or unpublish workflows.
    ConnectorNo auth
  • Delete an angle. Its concepts are deleted with it; ads keep their stamp (dangling but queryable). Scoped to the active Space — see set_active_space to switch, or pass space_id to override for this one call.
    Connector
    Destructive
    API key
  • Store one or more new memories (concepts, decisions, findings). Before filing, search for related content first — use the search results to infer the domain: if related memories exist in a domain, file there. Prefer existing domains over creating new ones; only propose a new domain if no related content is found anywhere. Single: pass fields directly — returns {memory, suggested_connections}. Batch: pass {items:[{label,domain,...},...]} — returns {memories:[{memory,suggested_connections},...]}. After filing, review suggested_connections for agreement or contradiction with what you just filed — not only for connect opportunities. Semantic similarity reflects aboutness, not agreement; the server surfaces candidates that may warrant your review but never asserts they conflict. When a filed memory is close enough to an existing memory that they may be worth comparing, the response also includes possible_contradicts=true and possible_contradicts_candidates (id, node_kind, semantic_distance, authority_severity) — same aboutness-not-agreement caveat: the server flags these as worth your attention, never as confirmed contradictions. Review them and call connect(relationship=contradicts) if you judge they actually conflict. ALWAYS call connect for any suggested_connections you accept before ending your session (batch: connect each accepted candidate). On failure, content[0].text is JSON: {"error_class": "conflict|retryable|forbidden|validation|internal", "message": "..."}. Switch on error_class: retry on retryable, surface message on validation, treat conflict as duplicate.
    ConnectorAPI key
  • One knowledge-graph entity's profile: name, kind, description, and Wikipedia link. Use it to confirm what a slug from `particle_entity_resolve` actually refers to — especially for the long tail that isn't a person or company (places, organizations, events, products, concepts). When the entity is a linked person or company the response carries the person_slug / company_slug — prefer `particle_person_get` / `particle_company_get` for those, which return the full profiles. Entity slugs feed `particle_podcast_find_mentions`, `particle_podcast_get_episode_timeseries`, and the alert tools.
    ConnectorOAuth
  • Fetch the full Markdown of one document by its site path (for example /docs/concepts/embeddings-101), as returned by search_docs.
    ConnectorNo auth
  • Map an API name to the current package, install line, import, and call shape (ChatOpenAI, create_agent, VectorStoreIndex, xrpToDrops, …). Use when you know the symbol but not where it lives. Prefer search_ai_framework_docs for concepts and fetch_latest_syntax for topic snippets. Paid tools/call: $0.001 USDC or 1000 drops XRP; catalog-backed.
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  • Returns a condensed 2-minute quick-start guide with minimal working examples, core syntax reference, and key concepts. Use this for rapid learning when you need to generate simple diagrams quickly.
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