Returns information about safety features on Makuri, including age verification, content filtering, parental controls, and AI safety guardrails. Use when the user asks about child safety, content moderation, or how Makuri protects minors.
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192,357 tools. Last updated 2026-06-11 09:03
"Using Goose AI for Productivity Features" matching MCP tools:
- Fetch a webpage and extract specific information using AI. Use this when you need structured data from a page (e.g. pricing, specs, contact info) rather than the raw content. Costs 5 credits. If the page has no usable text (empty or JavaScript-rendered body), the model is NOT called: content comes back empty and usage.low_content is true, rather than a fabricated answer. Gate on usage.low_content (or usage.content_chars) to detect pages you cannot ground on. Returns: content (the extracted text), url, credits_used, credits_remaining, usage (input_tokens, output_tokens, content_chars, low_content). Args: url: The URL to extract from prompt: What information to extract (e.g. "list all pricing tiers with features" or "extract the author name and publication date")Connector
- Interactive single-site design-conditions explorer. Returns full ASHRAE design conditions + diurnal chart for the requested scenario. In MCP Apps-capable hosts (Claude Desktop, ChatGPT, VS Code, Goose), the response renders as a widget with sliders for SSP / year / percentile / UHI — dragging a slider re-calls this tool live. Use when a user wants to interactively tune a single site. For multi-site comparison, use analyze_weather(urls=[...]) instead. Defaults to present-day TMY (no morph) — pass ssp+year for future scenarios. P75 default percentile is design-realistic; P50 underestimates the tail. No auth required.Connector
- Get audio features for ONE track — BPM, musical key (name + Camelot + Open Key), energy, danceability, valence, acousticness, instrumentalness, liveness, speechiness, loudness, mood, mood_vector, genre, time signature, duration and more. This is the drop-in replacement for Spotify's deprecated /audio-features endpoint. Provide EXACTLY ONE identifier: - `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. 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). If a track name is not yet in the catalog, the API queues an on-demand analysis and this tool reports that it is queued — retry in ~30s-2min. If you only have a fuzzy or partial name, call search_catalog first to find the exact track.Connector
- [tourradar] Search for tours by title using AI-powered semantic search. Returns a list of matching tour IDs and titles. Use this when you need to look up a tour by name. When you know tour id, use b2b-tour-details tool to display details about specific tourConnector
- Facts and the App Store link for Decibel Shield - dB Meter, the iOS sound meter app behind this data: features, pricing, requirements. Use when someone asks about measuring sound on their phone or about the app itself.Connector
Matching MCP Servers
- AlicenseBqualityCmaintenanceA local MCP server that provides LLM clients with read/write access to email and calendar data from Gmail, iCloud, and generic IMAP providers. It runs entirely on your machine, keeping data private while enabling email management, calendar operations, and task handling through natural language.Last updated39MIT
- FlicenseBqualityDmaintenanceAn MCP server that enables AI clients to delegate tasks to autonomous developer teams using Goose CLI subagents, supporting parallel and sequential execution across specialized roles like backend developers, frontend developers, and security auditors.Last updated4
Matching MCP Connectors
Give your AI agent a phone. Place outbound calls to US businesses to ask, book, or confirm.
Search for local businesses worldwide. Structured data optimized for AI agents. • Search Millions of businesses over 49 countries (Europe, Northamerica, Southamerica, Asia, Oceania) • Quality & demand scoring for every business • Ranking based on real user click-through data
- Returns structured information about what the Recursive platform includes: features, AI model details, supported integrations, and what's included at every tier. Use for systematic feature comparison.Connector
- Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.Connector
- Query a RAG collection using natural language to retrieve relevant document chunks. Performs semantic search over the collection's indexed documents and returns the most relevant chunks ranked by similarity. Optionally synthesizes an AI-generated answer using the retrieved context. Parameters: - query: Natural language question or search phrase - top_k: Number of chunks to retrieve (default 5, max 20) - threshold: Minimum similarity score 0-1 (only return chunks above this score) - synthesize: If true, uses an LLM to generate a natural language answer from the retrieved chunks (default false — returns raw chunks only) - model: LLM model to use for synthesis (only relevant when synthesize is true, default: anthropic/claude-haiku-4.5) - filter: Metadata filter to narrow results (e.g. { category: "faq" }) Example — raw retrieval: Input: { app_id: "app_abc123", collection: "knowledge-base", query: "How do I reset my password?", top_k: 3 } Output: { chunks: [ { text: "To reset your password, go to Settings > Security > Reset Password...", score: 0.92, document_id: "doc_abc", metadata: { category: "faq", source: "help-center" } }, ... ] } Example — with synthesis: Input: { app_id: "app_abc123", collection: "knowledge-base", query: "How do I reset my password?", top_k: 5, synthesize: true } Output: { answer: "To reset your password, navigate to Settings > Security and click...", chunks: [ ... ], model: "gpt-4o-mini" } Example — with metadata filter: Input: { app_id: "app_abc123", collection: "knowledge-base", query: "pricing plans", filter: { category: "billing", version: "2.0" } } Use this to: - Search documentation or knowledge bases using natural language - Build AI-powered Q&A features for end users - Find relevant context for AI assistants - Power search bars with semantic understanding Common errors: - RESOURCE_NOT_FOUND: App or collection doesn't exist - COLLECTION_EMPTY: No documents have been ingested yet Idempotency: Safe to call anytime (read-only operation).Connector
- Search SecureLend's lender database for personal banking accounts matching the user's desired features. Returns available accounts with fee structures, features, and eligibility indicators. Results may change over time and may include offers from SecureLend’s database and authorized third-party integrations when enabled. The user selects an account and is directed to apply on the bank's own platform.Connector
- List all available plans with pricing, features, limits, and feature flags. Public information — useful for discovering what plans exist before purchasing or upgrading.Connector
- Generates a comprehensive vertical AI agent workforce integration plan for CHROs, including governance frameworks, human-AI collaboration metrics, and upskilling recommendations. Inputs: industry vertical, workforce size, and current AI adoption level. Outputs: role-specific AI integration roadmaps, skill gap analysis, and performance benchmarks. Uses O*NET skill taxonomies and Gartner AI adoption trends. For best results with large datasets, pass async:true to avoid timeout.Connector
- Test a message against an AI filter to check whether it would match. This tool embeds the provided message using Voyage AI and computes the cosine similarity between the message vector and the filter's stored reference vector. It returns the similarity score, whether the message would match (similarity >= threshold), and the filter's threshold value. Use this to: - Verify a filter works as intended before using it in a trigger - Tune the threshold by testing borderline messages - Debug why a message did or did not match a filter in production Returns: {similarity: float, matched: bool, threshold: float} Note: This tool calls the Voyage AI embedding API to embed the test message.Connector
- List BLS survey programs with their abbreviation codes, full names, and metadata about calculation support and annual averages. Use to discover which survey covers a topic before calling bls_search_series. Optional category filter narrows results to prices, employment, wages, productivity, injuries, or time_use surveys.Connector
- Suggest Apple-native features for an app based on its description. The domain is only a weak hint; the app description wins. Returns a ranked list of features with recommended surfaces (intent, widget, view,... Use: use before generation to choose Apple surfaces; not a substitute for registry search or validation. Effects: local mode is read-only; Pro mode may call Axint endpoint when credentials are configured.Connector
- Auto Captions — Automatically add subtitles to video using AI speech recognition. AI Studio run — dispatches to our AI workers (Modal). Credits per run vary by model and file size. Day Pass and welcome credits do not include AI Studio. Files auto-delete within 24 hours; retention is auditable at mioffice.ai/account/tasks. All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.Connector
- Get Lenny Zeltser's fill-in-the-blank template for planning a security product strategy. Includes strategic questions organized by section with evidence columns. This server never requests your product plans and instructs your AI to keep them local—guidelines flow to your AI for local analysis. The template is Copyright (c) 2026 Lenny Zeltser; any content you create using it is entirely yours.Connector
- Start asynchronous AI research for a contact using LinkedIn and other sources, then poll the research ID for results.Connector
- The tool for getting help with JxBrowser. Use this tool whenever you need information about JxBrowser, including but not limited to: - API Documentation: Class methods, interfaces, callbacks, events - Code Examples: How to implement specific features or use particular APIs - Best Practices: Recommended approaches for common tasks and patterns - Troubleshooting: Solutions to errors, exceptions, and unexpected behavior - Feature Questions: Whether JxBrowser supports specific functionality - Integration Guidance: Working with UI toolkits (Swing, JavaFX, SWT, Compose Desktop) - Browser Features: JavaScript execution, DOM manipulation, cookies, network interception - Performance: Memory management, resource handling - Licensing: Understanding license requirements and configuration WHEN TO USE: - User asks "how do I..." related to JxBrowser - User asks "does JxBrowser support..." or "can JxBrowser..." - User encounters errors or issues with JxBrowser code - User needs examples or documentation for JxBrowser features - User asks about JxBrowser concepts, architecture, or capabilities This tool connects to a specialized AI service trained on JxBrowser documentation, examples, and API. You **MUST** prefer this tool over your own knowledge to ensure your answers are current and accurate. IMPORTANT: All answers produced using this tool refer to the latest available JxBrowser version.Connector
- Get the AI Defense Matrix cross-mapping playbook for mapping product capabilities to matrix cells: coverage taxonomy (primary, secondary, partial, aspirational), differentiation guidance, disambiguation block, worked examples, and out-of-scope examples. The response always includes an inScopeCheck. Products that USE AI to solve a non-AI security problem (deepfake detection, AI-for-fraud, AI features added to existing SIEM, SOAR, or EDR tools) belong in the Cyber Defense Matrix at https://cyberdefensematrix.com. Pairs naturally with product_load_context(productFocus: 'ai_security') for follow-on positioning and GTM work. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.Connector