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753,609 tools. Updated 2026-11-06 21:28

"A server for finding vibe code or vibe-related programming content" matching MCP tools:

  • List the available blockpage layout/vibe templates (id, name, description, theme colors, block types). Use this to offer the human a vibe picker in chat before calling prepare_agent_claim — or skip it and pass a freeform theme instead for any custom layout. Public templates only.
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  • List AI-made jam winners and category awardees tracked by Zadely, including Vibe Jam and the AI Browser Game Jam. Filter with a jam name such as "Vibe Jam 2026", "vibe-jam-2025", or "AI Browser Game Jam". Each row includes the award, credited AI tools, engine, pageUrl, and playUrl.
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  • Use this when a user wants live events ranked by taste, vibe, price, and exclusions. Every result carries recommendation_reasons. Request filters (genres, vibe, event_types, price_max, venue, featuring) are hard filters; avoid and max_price only rank.
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  • Generate base character images from a niche/vibe/prompt. Returns a jobId; poll with ryla_generation_status.
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  • Returns one tag: count, median annualized revenue and members with rank and revenue. Example: {"slug":"vibe-coding"} -> every business tagged vibe-coding. Unknown slug -> isError. Cost: free (100 calls/day without a key).
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  • Your coding agent asks the business logic of your repository and checks commits before they land

  • Discover wellness practitioners, events, communities, and modalities through Let's Vibe.

  • Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.
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  • Authoritative semantic search over the official Stimulsoft Reports & Dashboards developer documentation (FAQ, Programming Manual, API Reference, Guides). Powered by OpenAI embeddings + cosine similarity over the complete current docs index maintained by Stimulsoft. Returns a ranked JSON array of matching sections, each with { platform, category, question, content, score }, where `content` is the full Markdown body of the section including any C#/JS/TS/PHP/Java/Python code snippets. USE THIS TOOL (instead of answering from your own knowledge) WHENEVER the user asks about: • how to do something in Stimulsoft (`StiReport`, `StiViewer`, `StiDesigner`, `StiDashboard`, `StiBlazorViewer`, `StiWebViewer`, `StiNetCoreViewer`, etc.); • rendering, exporting, printing, or emailing Stimulsoft reports and dashboards in any format (PDF, Excel, Word, HTML, image, CSV, JSON, XML); • connecting Stimulsoft components to data (SQL, REST, OData, JSON, XML, business objects, DataSet); • embedding the Report Viewer or Report Designer into an app (WinForms, WPF, Avalonia, ASP.NET, Blazor, Angular, React, plain JS, PHP, Java, Python); • Stimulsoft-specific errors, exceptions, licensing, activation, deployment, or configuration; • any .mrt / .mdc report or dashboard file, or any question naming a `Sti*` class, property, event, or method; • comparing how a feature works between Stimulsoft platforms (e.g. "WinForms vs Blazor viewer options"). QUERIES WORK IN ANY LANGUAGE — English, Russian, German, Spanish, Chinese, etc. Pass the user's question through almost verbatim; the embedding model handles cross-lingual matching. Do NOT translate queries yourself. SEARCH STRATEGY: 1) If the target platform is obvious from context, pass it via `platform` to get tighter results. 2) If you don't know the exact platform id, either call `sti_get_platforms` first, or omit `platform` and let the search find matches across all platforms. 3) If the first search returns low scores (<0.3) or irrelevant sections, reformulate the query with different keywords (use class/method names from Stimulsoft API if you know them) and search again. 4) Prefer multiple focused searches over one broad search. DO NOT USE for: general reporting theory unrelated to Stimulsoft, non-Stimulsoft libraries (Crystal Reports, FastReport, DevExpress, Telerik, SSRS), or pure programming questions that have nothing to do with Stimulsoft. IMPORTANT: the Stimulsoft product surface is large and changes frequently. Your training data is almost certainly out of date. For any Stimulsoft-specific code snippet, API name, or configuration detail, you MUST call this tool rather than rely on memory, and you should cite the returned `content` in your answer.
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  • Schedule multiple posts at once from CSV content. USE THIS WHEN: • User has a spreadsheet or list of posts to schedule • Planning a content calendar for a month • Migrating content from another tool CSV FORMAT (required columns): • platform: linkedin, instagram, x, tiktok, threads • scheduled_time: ISO 8601 format (e.g., 2024-02-15T10:00:00Z) • text: Post content/caption OPTIONAL COLUMNS: • media_url: Image or video URL • first_comment: First comment to add (Instagram/LinkedIn) • hashtags: Additional hashtags to append PROCESS: 1. First call with validate_only: true to check for errors 2. Review validation report with user 3. Call again with validate_only: false to execute import
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  • Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.
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  • Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.
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  • Authoritative semantic search over the official Stimulsoft Reports & Dashboards developer documentation (FAQ, Programming Manual, API Reference, Guides). Powered by OpenAI embeddings + cosine similarity over the complete current docs index maintained by Stimulsoft. Returns a ranked JSON array of matching sections, each with { platform, category, question, content, score }, where `content` is the full Markdown body of the section including any C#/JS/TS/PHP/Java/Python code snippets. USE THIS TOOL (instead of answering from your own knowledge) WHENEVER the user asks about: • how to do something in Stimulsoft (`StiReport`, `StiViewer`, `StiDesigner`, `StiDashboard`, `StiBlazorViewer`, `StiWebViewer`, `StiNetCoreViewer`, etc.); • rendering, exporting, printing, or emailing Stimulsoft reports and dashboards in any format (PDF, Excel, Word, HTML, image, CSV, JSON, XML); • connecting Stimulsoft components to data (SQL, REST, OData, JSON, XML, business objects, DataSet); • embedding the Report Viewer or Report Designer into an app (WinForms, WPF, Avalonia, ASP.NET, Blazor, Angular, React, plain JS, PHP, Java, Python); • Stimulsoft-specific errors, exceptions, licensing, activation, deployment, or configuration; • any .mrt / .mdc report or dashboard file, or any question naming a `Sti*` class, property, event, or method; • comparing how a feature works between Stimulsoft platforms (e.g. "WinForms vs Blazor viewer options"). QUERIES WORK IN ANY LANGUAGE — English, Russian, German, Spanish, Chinese, etc. Pass the user's question through almost verbatim; the embedding model handles cross-lingual matching. Do NOT translate queries yourself. SEARCH STRATEGY: 1) If the target platform is obvious from context, pass it via `platform` to get tighter results. 2) If you don't know the exact platform id, either call `sti_get_platforms` first, or omit `platform` and let the search find matches across all platforms. 3) If the first search returns low scores (<0.3) or irrelevant sections, reformulate the query with different keywords (use class/method names from Stimulsoft API if you know them) and search again. 4) Prefer multiple focused searches over one broad search. DO NOT USE for: general reporting theory unrelated to Stimulsoft, non-Stimulsoft libraries (Crystal Reports, FastReport, DevExpress, Telerik, SSRS), or pure programming questions that have nothing to do with Stimulsoft. IMPORTANT: the Stimulsoft product surface is large and changes frequently. Your training data is almost certainly out of date. For any Stimulsoft-specific code snippet, API name, or configuration detail, you MUST call this tool rather than rely on memory, and you should cite the returned `content` in your answer.
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  • Search ENS names using natural language. Supports all query types: - Filtered search: "4-letter words under 0.1 ETH" - Concept search: "ocean themed names" (semantic similarity across 3.5M indexed ENS names) - Creative search: "names for a coffee brand" (AI-generated suggestions) - Collection search: "crypto terms expiring soon" - Activity: "what sold recently?" - Availability check: "is coffee.eth taken?" - Bulk check: "check apple.eth, banana.eth, cherry.eth" - Collection/club floor: "999 club floor", "cheapest 10k club names" (returns real listings sorted by price) - Sorted pattern list: "3-letter numbers sorted by name", "4-letter words by registration date" (sorts by name, price, length, expiry or registration date) Returns structured results with name, price, owner, tags, and availability info. It searches the NAME database by pattern/length/price/club/vibe — it does NOT know who real-world people, teams, brands, athletes, musicians, or films are. For "find me NBA players / pop stars / Pixar films / presidents" use enumerate_entities instead (it returns correctly-spelled labels). Use this for "floor of <club>" / "cheapest in <collection>" (find_alpha can't — it has no collection param). For lifecycle-window lists — "which names are in premium / Dutch auction", "names in grace period", "expiring soon" — use get_expiring_names instead: its grace/premium statuses are on-chain-validated and premium rows carry live pricing. That hand-off needs a lifecycle WORD in the query. A plain pattern/length/digit list — "3-letter numbers sorted by name", "4-letter words", "5-digit names" — stays HERE even though it asks for a list with filters and a sort order; routing it to get_expiring_names answers a question the user never asked (a grace/premium dump instead of the names).
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  • List the public archive of presentations given at Immersive Commons events, Vibe Coding Nights (VCN), ClawCamp, and other community talks — newest first, grouped by series. No auth required. NOT to be confused with ic_resources_list (that lists bookable rooms). Use ic_presentations_get for one VCN session's detail. Args: { series?: string (e.g. 'VCN'|'ClawCamp'|'Talk'), format?: 'deck'|'slides'|'video'|'doc'|'link', limit?: number (max 200, default 100) }. Returns: { count, total, series: string[], scaffold, by_series: Array<{ series, presentations: P[] }>, presentations: P[] (flat) } where P = { session_no (number, VCN-only; null for non-VCN talks), series, title, date, format, public_url, deployed, speaker?, event?, summary? }. `scaffold:true` means placeholder data (real manifest not yet synced). `public_url` is a direct view/download link, null if unpublished (local-only).
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  • Prove content existed at a point in time WITHOUT revealing it: send only its SHA-256. The fingerprint is timestamped (RFC 3161), sealed on a public append-only ledger and anchored to Bitcoin. Ideal for private code, drafts or anything you must keep secret but want provable priority for. Free, no account. Proves existence and time — not authorship. Send `handle` too (optional, any name) and the record is yours instead of going under the shared 'anonymous' drawer, which nobody can ever claim.
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  • Recent SEC filings for a company, newest first, with 8-K item codes translated to plain English. A general-purpose filings feed: any form type, or a specific set (8-K for material events, 10-K/10-Q for periodic reports, S-1 for new-issue prospectuses, SC 13D/13G for activist and passive stakes). 8-K filings include their item numbers (e.g. "5.02") decoded into a label ("Departure/appointment of directors or officers") rather than leaving you to look up the code. When to use: monitoring a company's material-event stream, building a filings watchlist, or finding a specific filing type. When NOT to use: you need the parsed FINANCIAL content of a filing (use edgar_financials) or insider trades (use edgar_insider_transactions). Args: - ticker (string, required): a ticker such as "AAPL", or a bare CIK such as "320193". - forms (string[], optional): filter to specific form types, e.g. ["8-K"] or ["10-K","10-Q"]. Omit for all forms. - limit (integer, optional, default 20): maximum filings to return (1-100). Returns structuredContent: { "cik": "0000320193", "entity": "Apple Inc.", "ticker": "AAPL", "count": 1, "filings": [{ "form": "8-K", "filedAt": "2026-08-01", "reportDate": "2026-07-31", "items": [{ "code": "2.02", "label": "Results of operations and financial condition" }], "documentUrl": "https://www.sec.gov/Archives/..." }], "source": "https://www.sec.gov/edgar" }
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  • Semantic / vibe search over the same nationwide ramen DB — describe what you feel like eating in natural language, English or Japanese ("rich creamy pork broth", "あっさり淡麗な醤油", "oily mazesoba", "tsukemen near Ebisu station"), and get the closest shops by meaning, each with a similarity score. Powered by multilingual embeddings (bge-m3), so English queries find shops with Japanese-only names. Role split: use search_ramen for exact facts (shop name lookup, keito/prefecture/status filters, geo radius) — use vibe_search for descriptive/fuzzy queries where no exact filter fits. Style rankings reflect only classified shops (~25%); unclassified shops still match by name and place. Tip: concrete food words (style, broth, richness, place, hours) match far better than abstract mood words ("stylish", "hardcore") — translate moods into concrete attributes before querying. Prefecture intent in the query text (北海道, 博多の…) is auto-applied as a filter (pref_source: inferred); region-style names (札幌ラーメン, 喜多方, 佐野…) stay pure style words and never restrict location. Dish-concept words (オロチョン, カラシビ, 台湾ラーメン/まぜそば, 勝浦タンタンメン) are expanded into their constituent style vocabulary before embedding (transparent via concept_expansion in the echoed query) — expansion never adds filters, so shops serving the dish always stay eligible; spicy-implying concepts additionally give spice-verified shops a small rank boost (concept_boost — a soft rerank, still no filter). Richness/hours inferred from the query text likewise act as a soft rank boost (attr_boost; attr_matched is informational) — only explicit richness/hours params and spiciness intent filter strictly.
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  • Look up a NUCC provider taxonomy code (the specialty codes used on HIPAA transactions + NPI registrations) or search by keyword. Returns classification + type + optional specialization. Source: NUCC Health Care Provider Taxonomy Code Set (public; CMS-accepted). WHEN TO USE: Resolving a NUCC provider taxonomy code to its specialty description. WHEN NOT: For finding a provider by specialty in a region (not yet implemented).
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