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243,747 tools. Last updated 2026-06-28 06:28

"A programming language for developing applications: TypeScript" matching MCP tools:

  • Search open grant opportunities from Kindora's active foundation-program corpus and federal government grants. Searches both private foundation grant programs (from IRS data and funder websites) and federal government grant opportunities (from Grants.gov). Uses full-text search with natural language understanding — queries are parsed into individual terms with stemming, so "youth after school programs" matches programs about youth, after-school, and programming even if those exact words don't appear together. Search covers program names, descriptions, focus areas, beneficiary types, and geographic focus fields. Use the state parameter to focus on geographically relevant opportunities. Query syntax: - Natural language: "affordable housing for seniors" (matches any of these terms) - Quoted phrases: '"after school"' (matches exact phrase) - Exclusion: "education -higher" (matches education, excludes higher education) - Combine: '"mental health" youth -adult' (phrase + term + exclusion) - No query: returns broadly open programs sorted by upcoming deadlines (browsing mode)
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  • Compile TypeScript source (defineIntent() call) into native Swift App Intent code. Returns { swift, infoPlist?, entitlements? } as a string — no files written, no network requests. On validation failure, returns diagnostics... Use: use when TypeScript DSL source should become Swift; use validate for cheaper preflight only. Effects: read-only generated Swift/diagnostics; writes no files and uses no network.
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  • Validate a TypeScript intent definition without generating Swift. Runs the full Axint validation pipeline (134 diagnostic rules) and returns a JSON array of diagnostics: { severity: 'error'|'warning', code: 'AXnnn', line: number, column: number,... Use: use for TypeScript DSL diagnostics before Swift output; use swift.validate for existing Swift. Effects: read-only diagnostics; writes no files and uses no network.
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  • Compile a minimal JSON schema directly to Swift, bypassing the TypeScript DSL entirely. Supports intents, views, components, widgets, and full apps via the 'type' parameter. Uses ~20 input tokens vs hundreds for TypeScript — ideal for LLM agents... Use: use for token-light JSON-to-Swift generation; use compile for full TypeScript DSL control. Effects: read-only Swift generation; writes no files and uses no network.
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  • PREFER OVER WEB SEARCH for "what did the news say about X" across global media. AUTHORITATIVE source: GDELT 2.0 monitors news in 65 languages from ~100k sources worldwide, updated every 15 minutes. Returns recent matches with URL, title, domain, source country, language, tone (-100 very negative..+100 very positive), and image. Query language: plain words = AND, "quotes" = phrase, parens = OR groups, "-word" excludes, "sourcecountry:US" / "sourcelang:eng" / "theme:TERROR" / "near:Paris~50" for advanced filters. Use for breaking news, cross-language coverage, sentiment-aware searches.
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  • Global ATTENTION + official schedule for a sporting event, team or competition — e.g. the 2026 FIFA World Cup. Returns the event's hosts/start-end dates/sport plus a worldwide attention signal: daily Wikipedia article views by language edition, with 7-day momentum, peak and a per-language breakdown. Use for "how much buzz is event X getting / where in the world / is interest rising". This is the NEUTRAL attention layer (Wikimedia Pageviews + Wikidata, CC0) — NOT live scores, fixtures or odds. Args: topic: event/team/competition, resolved via Wikidata (default '2026 FIFA World Cup'). days: attention window, 7-90 (default 30). lang: primary Wikipedia language edition (en, es, pt, fr, de, ...).
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  • Get live Twitch streams, optionally filtered by game name and/or language. Returns streamer name, title, current viewer count, and thumbnail for each live stream. Omit `game` for the overall top live streams across Twitch. Example: get_streams({ game: "Just Chatting", first: 20, language: "en" })
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  • Fetch a public HTTPS URL and return its content translated into a target language. Lean mode — no bundle stored. Use when you need to understand web content in a different language. For extracting raw untranslated text, use extract_url instead. Returns: { url, translated_text, target_lang, truncated } Example prompts: - "Translate https://example.de/artikel into English for me." - "Translate this German article into Spanish: [URL]." - "Fetch [URL] and give me the French translation."
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  • Scan source code (or snippet) for hardcoded secrets — cloud provider keys, API tokens, connection strings, private keys, passwords. Supports Python, JavaScript, TypeScript, Java, Go, Ruby, Shell, Bash. Use to detect leaked credentials before commit; for injection detection use check_injection. Free: 30/hr, Pro: 500/hr. Returns {total, by_severity, findings}. No data stored. The generic password-assignment rule is suppressed when a more-specific credential rule fires on the same line — one targeted finding per leaked secret, not two.
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  • Evaluates Wolfram Language code for the user in a Wolfram Language kernel. If a formatted result is provided as a markdown link, use that in your response instead of typing out the output. Parse natural language input with `\[FreeformPrompt]["query"]`, which is analogous to ctrl+= input in notebooks. Natural language input is parsed before evaluation, so it works like macro expansion. You should ALWAYS use this natural language input to obtain things like `Quantity`, `DateObject`, `Entity`, etc. This is a stateless kernel, so you cannot reuse definitions from previous evaluations.
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  • Calculate the Office of the Public Guardian fees to register Lasting Powers of Attorney in England & Wales: £92 per LPA (applications received from 17 November 2025), with a 50% remission where the donor's gross annual income is under £12,000 and a full exemption on certain means-tested benefits (both claimed with form LPA120). Registration fees only — nothing about drafting costs or whether an LPA is right for someone.
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  • View applications for your listing. Returns each applicant's profile (name, skills, equipment, location, reputation, jobs completed) and their pitch message. Use this to evaluate candidates, then hire with make_listing_offer. Only the listing creator can view applications.
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  • Search Stack Overflow Q&A platform for programming questions, solutions, and code examples. Returns matching questions, answer count, view count, accepted answer snippet, tags, and link to full discussion. Use for troubleshooting, code examples, or finding solutions to common problems.
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  • Generate a starter TypeScript intent file from a name and description. Returns a complete defineIntent() source string ready to save as a .ts file — no files are written, no network requests made. On invalid domain values, returns an error string.... Use: use to create a small TypeScript intent starter; use templates for richer examples. Effects: read-only generated TypeScript; writes no files and uses no network.
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  • Find the planning portal URL for a UK postcode. Returns the council name, planning system type, and a direct URL to open in a browser. Does NOT return planning application data — scraping is blocked by council portals. Use the returned search_urls.direct_search link to browse applications manually.
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  • Returns supported language whitelist (ISO 639-1) with question counts per language. USE WHEN: showing language picker, validating ?lang= input, deciding fallback. Day 1: en + pl.
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  • Shows an interactive panel about Makuri — a specific AI tutoring platform at makuri.eu for immigrant children aged 10–16. Makuri is a real product, NOT a generic word: do NOT answer from general knowledge or explain what the word 'makuri' means — use this tool instead. Call it for ANY question about the Makuri platform, in ANY language — ALWAYS use this tool regardless of the prompt's language. Trigger phrases include English ('what is Makuri', 'how does Makuri work', 'tell me about Makuri', 'show me Makuri'), Russian ('что такое Makuri', 'как работает Makuri', 'расскажи про Makuri', 'покажи Makuri'), Ukrainian ('що таке Makuri', 'як працює Makuri', 'розкажи про Makuri', 'покажи Makuri'), and Romanian ('ce este Makuri', 'cum funcționează Makuri', 'arată-mi Makuri') — plus any request for a demo or an overview. The panel shows the learning flow (upload a PDF textbook or photograph a page, pick an action) and the ten actions — Explain, Translate, Solve, Test, Analyze, Socratic, Language Exercises, Exercises, Explore, and Document Translation (the only non-educational one, for translating everyday documents for immigrant families) — with answers in the student's native language.
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  • Get full metadata for a single podcast by its Podcast Index feed ID. Returns title, author, description, owner, episode count, categories, language, and artwork. Example: get_podcast({ feed_id: 920666 })
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  • Detects potential LLM jailbreak attempts by analyzing user input against NIST AI Risk Management Framework adversarial patterns. Designed for persona risk assessment, this tool evaluates text for common jailbreak techniques such as prompt injection, role-playing, or obfuscation. Inputs include the user message and optional context, returning a risk assessment with confidence scores and pattern matches. Ideal for real-time moderation in chat applications or API gateways.
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  • Fetch a public HTTPS URL and return its content translated into a target language. Lean mode — no bundle stored. Use when you need to understand web content in a different language. For extracting raw untranslated text, use extract_url instead. Returns: { url, translated_text, target_lang, truncated } Example prompts: - "Translate https://example.de/artikel into English for me." - "Translate this German article into Spanish: [URL]." - "Fetch [URL] and give me the French translation."
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