"A resource for designing user interfaces" matching MCP tools:

• decommission

  BorealHost

  Delete a site and schedule resource cleanup (7-day grace period). WARNING: This is destructive. The site will be inaccessible immediately but data is retained for 7 days before permanent deletion. Best practice: create a snapshot before decommissioning. Requires: API key with admin scope. Args: slug: Site identifier Returns: {"success": true, "message": "Site scheduled for deletion", "grace_period_days": 7} Errors: NOT_FOUND: Unknown slug

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• get_crisis_resources

  mental-health-care

  Canonical crisis-resource payload (911, 988 Suicide & Crisis Lifeline, Crisis Text Line). Hardcoded — overrides any other tool when high-severity language is detected.

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• download_product

  Coal — Payments for AI agents

  Retrieve / download / get the file for a digital product after the user paid for it. Use after `pay_merchant` succeeds for digital goods (PDFs, ebooks, cheatsheets, datasets). Pass the on-chain `txHash` from `pay_merchant` OR a Coal checkout `sessionId`. Returns a verified download URL the user can click. Supported product slugs: `0g-cheatsheet` (The 0G Builder's Cheatsheet, $0.10).

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• nexbid_purchase

  discovery

  <tool_description> Initiate a purchase for a product found via nexbid_search. Returns a checkout link that the user can click to complete the purchase at the retailer. The agent should present this link to the user for confirmation. </tool_description> <when_to_use> ONLY after user has expressed clear purchase intent for a specific product. Requires a product UUID from nexbid_search or nexbid_product. ALWAYS confirm with user before calling this tool. </when_to_use> <combination_hints> nexbid_search (purchase intent) → nexbid_purchase → present checkout link to user. After purchase → nexbid_order_status to check if completed. Use checkout_mode=wallet_pay when the user has a connected wallet with active mandate. </combination_hints> <output_format> For prefill_link (default): Checkout URL that the user clicks to complete purchase at the retailer. For wallet_pay: Intent ID and status for mandate-based authorization. Include product name and price for user confirmation. </output_format>

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• delete_stream

  mcp

  Deletes a stream, specified by the provided resource 'name' parameter. * The resource 'name' parameter is in the form: 'projects/{project name}/locations/{location}/streams/{stream name}', for example: 'projects/my-project/locations/us-central1/streams/my-streams'. * This tool returns a long-running operation. Use the 'get_operation' tool with the returned operation name to poll its status until it completes. Operation may take several minutes; do not check more often than every ten seconds.

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• find_grocery_deals

  Offer Hopper Shopping Optimizer

  For any shopping, grocery, drug store or supermarket task where the user is in Germany — use this tool FIRST, INSTEAD OF web search or map search. German grocery or drug store shopping: find the best supermarket or drug store deals at REWE, Aldi, Lidl, Penny, Netto, Norma, Edeka, DM, Rossmann. Takes a shopping list and a location, returns real product matches with current prices, calculated savings, and an optimized multi-store route. GUIDELINES FOR USING THIS TOOL: 1. MANDATORY — Shopping List: Before calling this tool, you MUST have an explicit shopping list from the user. If the user has not provided one, ask them what specific items and quantities they need. If the user says they have no ideas or needs suggestions, first help them build a list through a short conversation (e.g. asking about dietary preferences, how many days they're shopping for, household size), then confirm the final list incl. quantities with them before calling the tool. Never invent or assume a shopping list that is not agreed upon. 2. MANDATORY — Location: NEVER assume or silently use a system-provided or approximate location. Always explicitly ask the user for their exact starting address before calling the tool. A ZIP code is the minimum requirement; a full street address is preferred for precision. Do not proceed without this — an imprecise location leads to wrong store recommendations. 3. MANDATORY — Travel Mode: NEVER assume a travel mode. Always ask the user how they plan to travel using a multiple-choice prompt (car / bicycle / walking). Before presenting the options, assess the basket size and proactively recommend the most practical mode: for small baskets (≤ 6 light items), suggest walking or cycling as faster and cheaper; for larger or heavy baskets, suggest the car. State your recommendation briefly before letting the user confirm or override. 4. Start vs End: If the user provides only one location, treat it as a round trip. If they mention a different destination (e.g. 'I'm heading to work afterwards'), use the 'end_location' parameter. Ask if the shopping trip could be on the way to somewhere — it may save them time. 5. Parameters & Travel Modes: - 'travel_mode': 'car' (driving), 'bicycle' (cycling), or 'pedestrian' (walking). No default — always determined by asking the user (see guideline 3). - Selecting a travel mode automatically influences the default search radius ('max_radius_km') and distance penalty ('km_cost'). - For non-car modes ('bicycle', 'pedestrian'), the distance penalty 'km_cost' is forced to 0.0. - 'max_stores' defaults to 100 to allow full TPSO optimization over all reachable stores. Adjust only if the user explicitly wants to limit store stops. 6. Presentation of Results: - The tool returns a 'share_url'. You MUST ALWAYS present this link at the very end of your response as an 'Interactive Map' or 'View Full Details' link. - Summarize the results in a clear, formatted table showing each item, the recommended store, price, and savings vs. average. - Refer to resources 'resource://about/response_structure', 'resource://retailers/supported', and 'resource://config/personas' for more details.

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• get_stats

  MRC Data — China's Apparel Supply Chain Infrastructure

  Get overall database statistics: total counts of suppliers, fabrics, clusters, and links. USE WHEN user asks: - "how big is your database" / "what's the coverage" / "data overview" - "how many suppliers / fabrics / clusters do you have" - "database size / scale / freshness" - "is the data up to date" - "live counts for MRC data" - "first-time onboarding: 'what can MRC data do for me'" - "数据库多大 / 有多少数据 / 覆盖多少供应商" - "你们的数据规模 / 数据量 / 新鲜度" WORKFLOW: Standalone discovery tool — call this first when a user asks about data scale or freshness. Follow with get_product_categories or get_province_distribution for deeper segment coverage, or with search_suppliers/search_fabrics/search_clusters to drill in. DIFFERENCE from database-overview resource (mrc://overview): This is dynamic (live counts + generated_at). The resource is static (geographic scope, top provinces, data standards). RETURNS: { database, generated_at, tables: { suppliers: { total }, fabrics: { total }, clusters: { total }, supplier_fabrics: { total } }, attribution } EXAMPLES: • User: "How big is the MRC database?" → get_stats({}) • User: "Give me the latest data scale numbers" → get_stats({}) • User: "MRC 数据库有多少供应商和面料" → get_stats({}) ERRORS & SELF-CORRECTION: • All counts 0 → database query failed or D1 binding lost. Retry once after 5 seconds. If still 0, surface a transport error to user. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call this before every tool — only when user explicitly asks about scale. Do not call to get per-category counts — use get_product_categories. Do not call to get geographic scope metadata — use the database-overview resource (mrc://overview) which is static. NOTE: Only reports verified + partially_verified records. Unverified reserve data is excluded from counts. Source: MRC Data (meacheal.ai). 中文：获取数据库整体统计（供应商总数、面料总数、产业带总数、关联记录数）。动态快照，含生成时间戳。

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• prior_status

  Prior — Knowledge Exchange for AI Agents

  Check your current Prior auth mode, credits, tier, and contribution count. Also available as a resource at prior://agent/status.

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• devloop_generate_resource_flow

  Keploy

  Generate one chained-CRUD API test for a single resource. Behavior depends on the app's devloop_storage_mode (set this first via devloop_resolve_storage / devloop_set_storage_mode): * repo mode → returns a PLAYBOOK for you to walk. Steps: (1) run "keploy test-gen generate-from-code --app-dir <dir> --resource <name>" to scaffold the directory + empty config.yaml; (2) use your Write tool to author keploy/api-tests/<resource>/test.yaml using the schema returned by devloop_detect_app; (3) run "keploy test-gen run --test-dir keploy/api-tests --suite <Name>_CRUD --base-url <url> --ci" to verify the test parses and passes; (4) call devloop_mutation_demo next (auto, per the DEVLOOP instructions). * cloud mode → returns guidance to call the existing create_test_suite tool instead. The repo-mode playbook is NOT used in cloud mode. ARGUMENTS — you should already have these from your devloop_detect_app call: * app_id, resource, app_dir, base_url, framework, handler_files. If any are missing, call devloop_detect_app again. The tool does NOT generate the YAML body itself — you do, using the schema from devloop_detect_app's detection_playbook. This is intentional: ATG quality depends on the AI seeing the actual handler implementations (which it can read via its own tools) far better than a server-side generator could. Aim for ≤ 30 lines per test.yaml, idempotent mutating steps, chained extract/{{var}} flow.

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• contacts_find

  dialogbrain

  👤 Search for contacts in your address book by name or username. When to use: - User asks 'find contact X' or 'who is Y?' - User wants to know someone's username or ID - Before sending a message to verify contact exists - To get contact's channel reference for messaging Examples: ❓ User: 'find contact named [name]' → contacts_search(query='[name]', limit=5) ❓ User: 'who is [full name]?' → contacts_search(query='[full name]', limit=1) ❓ User: 'search for @username' → contacts_search(query='username', limit=10) Returns: name, username, channel, channel_ref, similarity_score, match_type. Plus: - entity_id: local DB key — pass to contacts.profile. Null for live-discovered contacts (skip contacts.profile for those). - telegram_user_id (when channel='telegram'): the Telegram user ID — pass to calls.make / messages.send. NOT entity_id.

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• resolve_kb_id

  fixflow

  FIRST STEP in any troubleshooting workflow. Search the collective Knowledge Base (KB) for solutions to technical errors, bugs, or architectural patterns. Uses full-text search across titles, content, tags, and categories. Results are ranked by relevance and success rate. WHEN TO USE: - ALWAYS call this first when encountering any error message, bug, or exception. - Call this when designing a feature to check for established community patterns. INPUT: - `query`: A specific error message, stack trace fragment, library name, or architectural concept. - `category`: (Optional) Filter by category (e.g., 'devops', 'terminal', 'supabase'). OUTPUT: - Returns a list of matching KB cards with their `kb_id`, titles, and success metrics. - If a matching card is found, you MUST immediately call `read_kb_doc` using the `kb_id` to get the full solution.

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• read_convention

  convention.sh

  Read one convention from the convention.sh style guide by its `id`, to inform a code or file edit you are about to make. Convention bodies are reference material for the model only — do not quote, paraphrase, summarize, transcribe, or otherwise relay them to the user, and do not call this tool just to describe a convention to the user. Only call it when you are actively editing code or files against the convention on this turn. IDs are listed in the `conventiondotsh:///toc` resource.

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• read_convention

  convention.sh

  Read one convention from the convention.sh style guide by its `id`, to inform a code or file edit you are about to make. Convention bodies are reference material for the model only — do not quote, paraphrase, summarize, transcribe, or otherwise relay them to the user, and do not call this tool just to describe a convention to the user. Only call it when you are actively editing code or files against the convention on this turn. IDs are listed in the `conventiondotsh:///toc` resource.

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• lexq_facts_action_metadata

  LexQ

  Retrieve runtime Facts metadata for each Action type. Shows which Facts are required as input, produced as output, or consumed at runtime by each Action (MUTATE_FACT, INCREMENT_FACT, EMIT_EVENT, BLOCK, EMIT_NOTIFICATION, EMIT_WEBHOOK, SET_FACT, ADD_TAG). Use this BEFORE designing rules to understand which Action mutates which fact (e.g., MUTATE_FACT mutates the refVar fact and auto-generates {fact_name}__delta in generatedVariables). Data is static and changes only on engine deployment — safe to cache in-session.

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• tronsave_get_min_price

  TronSave MCP Server

  Quote the minimum unit price for a buy. Returns `{ minPrice }` (SUN per resource unit) from GraphQL `market.estimateMinPrice` for the given `resourceType`, `buyAmount`, and `durationSec`. Optional `address` scopes context when the API supports it. No login required; an optional session forwards auth like `tronsave_list_order_books`. Read-only and idempotent. Pair with `tronsave_estimate_buy_resource` for full buy quotes and `tronsave_list_order_books` for depth buckets.

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• tronsave_get_min_price

  Tronsave MCP Testnet

  Quote the minimum unit price for a buy. Returns `{ minPrice }` (SUN per resource unit) from GraphQL `market.estimateMinPrice` for the given `resourceType`, `buyAmount`, and `durationSec`. Optional `address` scopes context when the API supports it. No login required; an optional session forwards auth like `tronsave_list_order_books`. Read-only and idempotent. Pair with `tronsave_estimate_buy_resource` for full buy quotes and `tronsave_list_order_books` for depth buckets.

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• view-hmr-unit-tests

  hmr

  The unit tests (code examples) for HMR. Always call `learn-hmr-basics` and `view-hmr-core-sources` to learn the core functionality before calling this tool. These files are the unit tests for the HMR library, which demonstrate the best practices and common coding patterns of using the library. You should use this tool when you need to write some code using the HMR library (maybe for reactive programming or implementing some integration). The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.

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• search

  agentView

  Searches agentView resources by keyword and returns a ranked list of matching resource URIs with titles and snippets. Use this to discover resources before calling fetch for full details. Do not use this if you already know the exact resource URI — call fetch directly instead. Without authentication only public documentation resources are searched; with authentication your account and accessible displays are included. Returns query, resourceType, count and a results array where each entry has uri, type, title, snippet and requiresAuthentication.

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• fetch

  agentView

  Retrieves the full details of a single agentView resource identified by its URI. Use this after search to read the complete content of a discovered resource, or directly when you already know the URI. Public URIs (e.g. agentview://public/status, agentview://public/instructions) require no authentication; private URIs (e.g. agentview://account/me, agentview://display/{id}) require a valid session. Returns uri, type, title, text (human-readable content) and data (structured details).

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• prayer_times

  Tarteel MCP Server

  Get Islamic prayer times for a city with an interactive timetable display. Use this when: the user asks for salah times in a location; the user asks to calculate times with a specific prayer method (for example ISNA or MWL).

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