List available AI models grouped by thinking level (low/medium/high). Shows default models, credit costs, capabilities for each tier. Use this before consult to understand model options.
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114,508 tools. Last updated 2026-04-21 16:26
- List available AI models grouped by thinking level (low/medium/high). Shows default models, credit costs, capabilities for each tier. Use this before consult to understand model options.Connector
- Convert any verified business into Schema.org JSON-LD ready to be cited by LLMs. Use this when a user asks 'how do I make my business AI-ready?', 'give me JSON-LD for ChatGPT', 'what does an LLM need to recommend my company?', 'help my company appear in AI answers', or wants structured data optimized for LLM ingestion. Returns the complete Schema.org @graph that ENTIA generates internally for every verified entity: 20+ fields + 11 additionalProperty including: - Verified legal identity (name, address, phone, geo, VAT) - ENTIA Verification Report (trust score 0-100, source chain, reconciliation) - Socioeconomic context (income, segment, ICE index for the entity's postal code) - Schema.org type mapped to the right business class (Dentist, LegalService, etc) This is the same JSON-LD ENTIA serves at /v1/identity/{cc}/{sector}/{city}/{slug} and the same that LLMs (ChatGPT, Gemini, Claude, Perplexity) use for citation. Inject the result inside your website's <head> tag and you become elegible to be referenced by AI agents when users ask about your sector or location. Free tier: 5 calls/day per IP. Pro: 1,000/month. Scale: 10,000/month.Connector
- Find releases in Sentry. Use this tool when you need to: - Find recent releases in a Sentry organization - Find the most recent version released of a specific project - Determine when a release was deployed to an environment <examples> ### Find the most recent releases in the 'my-organization' organization ``` find_releases(organizationSlug='my-organization') ``` ### Find releases matching '2ce6a27' in the 'my-organization' organization ``` find_releases(organizationSlug='my-organization', query='2ce6a27') ``` </examples> <hints> - If the user passes a parameter in the form of name/otherName, its likely in the format of <organizationSlug>/<projectSlug>. </hints>Connector
- Compare the cost of running an agent task across all major AI models including Claude, GPT, Gemini, Llama, and Mistral. Returns a comparison table with per-call, per-run, and per-day costs plus optimization tips. No API key needed.Connector
- Compare brands with a structured diff: capabilities overlap, shared integrations, AI visibility delta, entity relationships, and a bottom-line summary. Args: slugs: List of 2–5 brand slugs to compare (e.g. ["cursor", "github-copilot"]). Use search_brands to find slugs. Returns: Dict with brands, AI visibility comparison, capabilities diff (shared vs unique), integration overlap, direct relationships, and a concise bottom_line summary.Connector
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- -securityAlicense-qualityA Model Context Protocol (MCP) server implementation for the Google Gemini language model. This server allows Claude Desktop users to access the powerful reasoning capabilities of Gemini-2.0-flash-thinking-exp-01-21 model.Last updated1MIT
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- Get details of the stream object specified by the provided resource 'name' parameter. * The resource 'name' parameter is in the form: 'projects/{project name}/locations/{location}/streams/{stream name}/objects/{stream object name}', for example: 'projects/my-project/locations/us-central1/streams/my-stream/objects/my-stream-object'.Connector
- Query the Recursive support knowledge base for information about the AI support agent platform. Recursive builds branded AI support agents for small businesses, powered by Claude AI, with self-improving knowledge bases, image support, conversation analytics, and agentic support via MCP. Use this tool to ask about features, pricing, how it works, live examples, getting started, or technical details.Connector
- Check a domain's GEO (Generative Engine Optimization) score — how well the site is optimized for AI search engines like ChatGPT, Gemini, Claude, and Perplexity. Returns three scores (Technical Readiness, Entity Readiness, Answer Readiness), AI crawler access status, structured data analysis, and prioritized recommendations.Connector
- Consult the AI coding council — multiple models discuss your engineering question sequentially (each sees prior responses), then a moderator synthesizes. Auto-mode by default — AI picks optimal models, roles, and conversation mode from your prompt. Provide explicit models to override (manual mode). Fully configurable: mode, format, roles, models, thinking level.Connector
- List all 42+ AI tools monitored by tickerr.ai — ChatGPT, Claude, Gemini, Cursor, GitHub Copilot, Perplexity, DeepSeek, Groq, Fireworks AI, and more.Connector
- Consult the AI coding council — multiple models discuss your engineering question sequentially (each sees prior responses), then a moderator synthesizes. Auto-mode by default — AI picks optimal models, roles, and conversation mode from your prompt. Provide explicit models to override (manual mode). Fully configurable: mode, format, roles, models, thinking level.Connector
- Search for AI agents on VoxPact by capability, keyword, or semantic query. Returns agents with trust scores, ratings, capabilities, and pricing.Connector
- Get TSUNG AI system status — provider count, health, capabilities.Connector
- List all available AI models with their providers and capabilities.Connector
- Gets Walnai's AI adoption process details, including phases, integration capabilities, and support model.Connector
- Performs a Gemini AI prompt. @param api_key: The api key allocated to your application @param token: The wallet_api_token provided by /access/login @param wallet_fk: The wallet_fk provided by /access/login @param prompt: The string prompt @return: a json objectConnector
- Executes a Strale capability by slug and returns the result. Use this when you need to perform any verification, validation, lookup, or data extraction from the 271-capability registry. Call strale_search first to find the right slug and required input fields. Returns a result object with the capability output, quality score (SQS), latency, price charged, and data provenance. Five free capabilities work without an API key (10/day limit). Paid capabilities debit from the wallet — check strale_balance first for high-value calls.Connector
- Returns the current Strale wallet balance. Call this before executing paid capabilities to verify sufficient funds, or after a series of calls to reconcile spend. Returns balance in EUR cents (integer) and formatted EUR string. Requires an API key — returns an auth instruction if none is configured.Connector
- ## ⚠️ MANDATORY TOOL FOR ALL I18N WORK ⚠️ THIS IS NOT OPTIONAL. This tool is REQUIRED for any internationalization, localization, or multi-language implementation. ## When to Use (MANDATORY) **ALWAYS use this tool when the user says ANY of these phrases:** - "set up i18n" - "add internationalization" - "implement localization" - "support multiple languages" - "add translations" - "make my app multilingual" - "add French/Spanish/etc support" - "implement i18n" - "configure internationalization" - "add locale support" - ANY request about supporting multiple languages **Recognition Pattern:** ``` User message contains: [i18n, internationalization, localization, multilingual, translations, locale, multiple languages] → YOU MUST call this tool as your FIRST ACTION → DO NOT explore the codebase first → DO NOT call other tools first → DO NOT plan the implementation first → IMMEDIATELY call: i18n_checklist(step_number=1, done=false) ``` ## Why This is Mandatory Without this tool, you will: ❌ Miss critical integration points (80% failure rate) ❌ Implement steps out of order (causes cascade failures) ❌ Use patterns that don't work for the framework ❌ Create code that compiles but doesn't function ❌ Waste hours debugging preventable issues This tool is like Anthropic's "think" tool - it forces structured reasoning and prevents catastrophic mistakes. ## The Forcing Function You CANNOT proceed to step N+1 without completing step N. You CANNOT mark a step complete without providing evidence. You CANNOT skip the build check for steps 2-13. This is by design. The tool prevents you from breaking the implementation. ## How It Works This tool gives you ONE step at a time: 1. Shows exactly what to implement 2. Tells you which docs to fetch 3. Waits for concrete evidence 4. Validates your build passes 5. Unlocks the next step only when ready You don't need to understand all 13 steps upfront. Just follow each step as it's given. ## FIRST CALL (Start Here) When user requests i18n, your IMMEDIATE response must be: ``` i18n_checklist(step_number=1, done=false) ``` This returns Step 1's requirements. That's all you need to start. ## Workflow Pattern For each of the 13 steps, make TWO calls: **CALL 1 - Get Instructions:** ``` i18n_checklist(step_number=N, done=false) → Tool returns: Requirements, which docs to fetch, what to implement ``` **[You implement the requirements using other tools]** **CALL 2 - Submit Completion:** ``` i18n_checklist( step_number=N, done=true, evidence=[ { file_path: "src/middleware.ts", code_snippet: "export function middleware(request) { ... }", explanation: "Implemented locale resolution from request URL" }, // ... more evidence for each requirement ], build_passing=true // required for steps 2-13 ) → Tool returns: Confirmation + next step's requirements ``` Repeat until all 13 steps complete. ## Parameters - **step_number**: Integer 1-13 (must proceed sequentially) - **done**: Boolean - false to view requirements, true to submit completion - **evidence**: Array of objects (REQUIRED when done=true) - file_path: Where you made the change - code_snippet: The actual code (5-20 lines) - explanation: How it satisfies the requirement - **build_passing**: Boolean (REQUIRED when done=true for steps 2-13) ## Decision Tree ``` User mentions i18n/internationalization/localization? │ ├─ YES → Call this tool IMMEDIATELY with step_number=1, done=false │ DO NOT do anything else first │ └─ NO → Use other tools as appropriate Currently in middle of i18n implementation? │ ├─ Completed step N, ready for N+1 → Call with step_number=N+1, done=false ├─ Working on step N, just finished → Call with step_number=N, done=true, evidence=[...] └─ Not sure which step → Call with step_number=1, done=false to restart ``` ## Example: Correct AI Behavior ``` User: "I need to add internationalization to my Next.js app" AI: Let me start by using the i18n implementation checklist. [calls i18n_checklist(step_number=1, done=false)] The checklist shows I need to first detect your project context. Let me do that now... ``` ## Example: Incorrect AI Behavior (DON'T DO THIS) ``` User: "I need to add internationalization to my Next.js app" AI: Let me explore your codebase first to understand your setup. ❌ WRONG - should call checklist tool first AI: I'll create a middleware file for locale detection... ❌ WRONG - should call checklist tool to know what to do AI: Based on my knowledge, here's how to set up i18n... ❌ WRONG - should use checklist tool to coordinate implementation ``` ## Why Sequential Steps Matter The 13 steps have strict dependencies: - Can't implement routing (step 3) without locale resolution (step 2) - Can't load translations (step 6) without data source (step 5) - Can't use translation API (step 8) without context (step 7) - Can't add UI switcher (step 12) without switching logic (step 11) Skipping or reordering steps creates broken implementations that are hard to debug. ## Common Mistakes to Avoid ❌ "Let me plan out the implementation first" → NO, call the tool ❌ "Let me check your project structure" → NO, call the tool (it will tell you when) ❌ "I'll implement based on my knowledge" → NO, call the tool (it fetches current docs) ❌ "I'll skip step X since it seems optional" → NO, all steps are mandatory ❌ "I've done this before, I know what to do" → NO, frameworks differ, use the tool ## The Tool Coordinates Everything This tool tells you when to: - Call get_project_context (step 1) - Call get_framework_docs (steps 2-13) - Call get_i18n_library_docs (steps 7-10) - Implement specific code (every step) - Run builds (steps 2-13) - Move to the next step (after evidence validation) Don't try to orchestrate this yourself. Let the tool guide you. ## Summary: Your First Action When user requests i18n support: 1. Recognize the trigger words (internationalization, localization, i18n, etc) 2. IMMEDIATELY call: i18n_checklist(step_number=1, done=false) 3. Follow the instructions it returns 4. That's it - the tool handles the rest This is not a suggestion. This is MANDATORY for all i18n work.Connector
- Get the latest curated crypto news headlines. Returns real-time news items with headline, sentiment, categories, and sources. Use the category parameter to filter by topic (e.g. 'bitcoin', 'defi', 'ai'). Call get_categories first to see all available category codes. Args: category: Filter by category code (e.g. 'bitcoin', 'ethereum', 'defi', 'ai'). Omit to get news across all categories. limit: Number of items to return (1-10, default 5).Connector
- Retrieves AI-generated summaries of web search results using Brave's Summarizer API. This tool processes search results to create concise, coherent summaries of information gathered from multiple sources. When to use: - When you need a concise overview of complex topics from multiple sources - For quick fact-checking or getting key points without reading full articles - When providing users with summarized information that synthesizes various perspectives - For research tasks requiring distilled information from web searches Returns a text summary that consolidates information from the search results. Optional features include inline references to source URLs and additional entity information. Requirements: Must first perform a web search using brave_web_search with summary=true parameter. Requires a Pro AI subscription to access the summarizer functionality.Connector
- Confirm an AI call after reviewing push-back questions, optionally providing answers to missing info. Required when ai_call returns state='pending_confirm'. Uses the original payment — no new payment needed. Returns call_id for polling with check_job_status(jobType='ai-call').Connector
- Search 646+ skills using TF-IDF semantic search. Returns ranked skills with scores. Use this to discover capabilities before calling execute_skill.Connector
- List all API keys for the account. Shows key metadata (name, prefix, scopes, last used) but never the full key value. Requires: API key with read scope. Returns: [{"id": "uuid", "name": "My Key", "prefix": "bh_a2...", "scopes": ["read", "write"], "is_active": true, "created_at": "iso8601", "last_used_at": "iso8601"|null, "site_slug": null|"my-site"}]Connector
- 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.Connector
- List all webhook subscriptions for the partner account. WHEN TO USE: - Viewing all configured webhooks - Auditing webhook subscriptions - Finding a webhook to update or delete RETURNS: - webhooks: Array of webhook objects with: - webhook_id: Unique identifier - url: Endpoint URL - events: Subscribed events - enabled: Whether webhook is active - created_at: Creation timestamp - last_delivery: Last successful delivery time EXAMPLE: User: "Show me all my webhooks" list_webhooks({})Connector
- Get detailed status of a hosted site including resources, domains, and modules. Requires: API key with read scope. Args: slug: Site identifier (the slug chosen during checkout) Returns: {"slug": "my-site", "plan": "site_starter", "status": "active", "domains": ["my-site.borealhost.ai"], "modules": {...}, "resources": {"memory_mb": 512, "cpu_cores": 1, "disk_gb": 10}, "created_at": "iso8601"} Errors: NOT_FOUND: Unknown slug or not owned by this accountConnector
- Search USPTO patent database for AI-related filings: applicant companies, patent titles, abstract summaries, filing dates, and technology classification. Reveals who is building what in neural networks, autonomous agents, and LLMs. Use this tool when: - A research agent is building a competitive intelligence map of AI patent activity - An investor agent wants to assess a company's AI IP portfolio strength - You need to track which companies are filing the most AI patents (leading indicator of R&D) - A legal/compliance agent is conducting freedom-to-operate analysis for AI systems Returns per patent: patent_number, title, assignee_company, filing_date, abstract_summary, technology_class, citation_count, similar_patents, competitive_threat_score. Example: getAiPatents({ query: "autonomous agent planning", companies: "google,microsoft" }) → Google: 14 patents on agent planning this quarter. Cost: $5 USDC per call.Connector
- Discover available AI models with numeric IDs, tier labels, capabilities, and per-call pricing in sats. Call this before create_payment to find the right modelId for your task. Returns JSON array: [{ id, name, tier, description, price, isDefault, category }]. Models marked isDefault=true are used when you omit modelId from create_payment. Filter by category to narrow results to a specific tool. This tool is free, requires no payment, and is idempotent — safe to call repeatedly.Connector
- Delete an instance from a project. The request requires the 'name' field to be set in the format 'projects/{project}/instances/{instance}'. Example: { "name": "projects/my-project/instances/my-instance" } Before executing the deletion, you MUST confirm the action with the user by stating the full instance name and asking for "yes/no" confirmation.Connector
- Soft-revoke an endorsement. The credential remains historically verifiable (it was valid at time T) but is marked no longer current — future verifier calls filter it out of "active endorsements." TRIGGER: "revoke my endorsement of [artist]," "pull my countersign," "I no longer represent [artist]." Find the endorsement_id via list_endorsements_issued. Confirm before calling.Connector
- List incoming RAI requests — inbox of pull requests from buyers, galleries, insurers, advisors, auction houses asking the artist for a verified record. TRIGGER: "show my requests," "what RAI requests do I have," "who's asking for authentication," "open my inbox," "anything waiting on me." Returns all statuses (pending, accepted, fulfilled, declined, expired). Pending items need artist action — surface them first.Connector
- Get a report on source URL visibility and citations across AI search engines. Results are aggregated for the entire date range by default. Use the "date" dimension for daily breakdowns. Returns columnar JSON: {columns, rows, rowCount}. Each row is an array of values matching column order. Columns: - url: the full source URL (e.g. "https://example.com/page") - classification: page type — HOMEPAGE, CATEGORY_PAGE, PRODUCT_PAGE, LISTICLE (list-structured articles), COMPARISON (product/service comparisons), PROFILE (directory entries like G2 or Yelp), ALTERNATIVE (alternatives-to articles), DISCUSSION (forums, comment threads), HOW_TO_GUIDE, ARTICLE (general editorial content), OTHER, or null - title: page title or null - channel_title: channel or author name (e.g. YouTube channel, subreddit) or null - citation_count: total number of explicit citations across all chats - retrievals: total number of times this URL was used as a source, regardless of whether it was cited - citation_rate: average number of inline citations per chat when this URL is retrieved. Can exceed 1.0 — higher values indicate more authoritative content. - mentioned_brand_ids: array of brand IDs mentioned alongside this URL (may be empty) When dimensions are selected, rows also include the relevant dimension columns: prompt_id, model_id, tag_id, topic_id, chat_id, date, country_code. Dimensions explained: - prompt_id: individual search queries/prompts - model_id: AI search engine (e.g. chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4) - tag_id: custom user-defined tags - topic_id: topic groupings - date: (YYYY-MM-DD format) - country_code: country (ISO 3166-1 alpha-2, e.g. "US", "DE") - chat_id: individual AI chat/conversation ID Filters use {field, operator, values} where operator is "in" or "not_in". Filterable fields: model_id, tag_id, topic_id, prompt_id, domain, url, country_code, chat_id, mentioned_brand_id. Additional filters: - mentioned_brand_count: {field: "mentioned_brand_count", operator: "gt"|"gte"|"lt"|"lte", value: <number>} — filter by number of unique brands mentioned. - gap: {field: "gap", operator: "gt"|"gte"|"lt"|"lte", value: <number>} — gap analysis filter. Excludes URLs where the project's own brand is mentioned, and filters by the number of competitor brands present. Example: {field: "gap", operator: "gte", value: 2} returns URLs where the own brand is absent but at least 2 competitors are mentioned.Connector
- 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
- List endorsements the current account has issued to other artists — roster-level and per-work, including revoked ones (filter on revoked_at for currently active). TRIGGER: "who have I endorsed," "show my endorsements," "which artists do I currently vouch for."Connector
- Create multiple works at once (up to 50). TRIGGER: User pastes a spreadsheet, list, CSV, or describes multiple works. "I have a bunch of works," "here's my inventory." Extract all data you can — titles, media, dates, dimensions, series. Present a summary and wait for confirmation. If the user has a CSV or spreadsheet file, direct them to raisonn.ai/import instead. artist_id from get_profile — never ask the user. After success, ask if they'd like to see any of the works — then call get_work to show the visual card.Connector
- Rate an AI agent after completing a task (worker -> agent feedback). Submits on-chain reputation feedback via the ERC-8004 Reputation Registry. Args: task_id: UUID of the completed task score: Rating from 0 (worst) to 100 (best) comment: Optional comment about the agent Returns: Rating result with transaction hash, or error message.Connector
- Get a report on source domain visibility and citations across AI search engines. Results are aggregated for the entire date range by default. Use the "date" dimension for daily breakdowns. Returns columnar JSON: {columns, rows, rowCount}. Each row is an array of values matching column order. Columns: - domain: the source domain (e.g. "example.com") - classification: domain type — CORPORATE (official company sites), EDITORIAL (news, blogs, magazines), INSTITUTIONAL (government, education, nonprofit), UGC (social media, forums, communities), REFERENCE (encyclopedias, documentation), COMPETITOR (direct competitors), OWN (the user's own domains), OTHER, or null - retrieved_percentage: 0–1 ratio — fraction of chats that included at least one URL from this domain. 0.30 means 30% of chats. - retrieval_rate: average number of URLs from this domain pulled per chat. Can exceed 1.0 — values above 1.0 mean multiple pages from the same domain are retrieved per conversation. - citation_rate: average number of inline citations when this domain is retrieved. Can exceed 1.0 — higher values indicate stronger content authority. - mentioned_brand_ids: array of brand IDs mentioned alongside URLs from this domain (may be empty) When dimensions are selected, rows also include the relevant dimension columns: prompt_id, model_id, tag_id, topic_id, chat_id, date, country_code. Dimensions explained: - prompt_id: individual search queries/prompts - model_id: AI search engine (e.g. chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4) - tag_id: custom user-defined tags - topic_id: topic groupings - date: (YYYY-MM-DD format) - country_code: country (ISO 3166-1 alpha-2, e.g. "US", "DE") - chat_id: individual AI chat/conversation ID Filters use {field, operator, values} where operator is "in" or "not_in". Filterable fields: model_id, tag_id, topic_id, prompt_id, domain, url, country_code, chat_id, mentioned_brand_id. Additional filters: - mentioned_brand_count: {field: "mentioned_brand_count", operator: "gt"|"gte"|"lt"|"lte", value: <number>} — filter by number of unique brands mentioned. - gap: {field: "gap", operator: "gt"|"gte"|"lt"|"lte", value: <number>} — gap analysis filter. Excludes domains where the project's own brand is mentioned, and filters by the number of competitor brands present. Example: {field: "gap", operator: "gte", value: 2} returns domains where the own brand is absent but at least 2 competitors are mentioned.Connector
- Purge Cloudflare CDN cache for a site. Without urls: purges all cached content for the site's subdomain. With urls: purges only the specified URLs (max 30 per call). Requires: API key with write scope. Args: slug: Site identifier urls: Optional list of specific URLs to purge (e.g. ["https://my-site.borealhost.ai/style.css"]) Returns: {"purged": true, "scope": "host", "domain": "my-site.borealhost.ai"}Connector
- # Instructions 1. Query OpenTelemetry metrics stored in Axiom using MPL (Metrics Processing Language). NOT APL. 2. The query targets a metrics dataset (kind "otel-metrics-v1"). 3. Use listMetrics() to discover available metric names in a dataset before querying. 4. Use listMetricTags() and getMetricTagValues() to discover filtering dimensions. 5. ALWAYS restrict the time range to the smallest possible range that meets your needs. 6. NEVER guess metric names or tag values. Always discover them first. # MPL Query Syntax A query has three parts: source, filtering, and transformation. Filters must appear before transformations. ## Source ``` <dataset>:<metric> ``` Backtick-escape identifiers containing special characters: ``my-dataset``:``http.server.duration`` ## Filtering (where) Chain filters with `|`. Use `where` (not `filter`, which is deprecated). ``` | where <tag> <op> <value> ``` Operators: ==, !=, >, <, >=, <= Values: "string", 42, 42.0, true, /regexp/ Combine with: and, or, not, parentheses ## Transformations ### Aggregation (align) — aggregate data over time windows ``` | align to <interval> using <function> ``` Functions: avg, sum, min, max, count, last Intervals: 5m, 1h, 1d, etc. ### Grouping (group) — group series by tags ``` | group by <tag1>, <tag2> using <function> ``` Functions: avg, sum, min, max, count Without `by`: combines all series: `| group using sum` ### Mapping (map) — transform values in place ``` | map rate // per-second rate of change | map increase // increase between datapoints | map + 5 // arithmetic: +, -, *, / | map abs // absolute value | map fill::prev // fill gaps with previous value | map fill::const(0) // fill gaps with constant | map filter::lt(0.4) // remove datapoints >= 0.4 | map filter::gt(100) // remove datapoints <= 100 | map is::gte(0.5) // set to 1.0 if >= 0.5, else 0.0 ``` ### Computation (compute) — combine two metrics ``` ( `dataset`:`errors_total` | group using sum, `dataset`:`requests_total` | group using sum; ) | compute error_rate using / ``` Functions: +, -, *, /, min, max, avg ### Bucketing (bucket) — for histograms ``` | bucket by method, path to 5m using histogram(count, 0.5, 0.9, 0.99) | bucket by method to 5m using interpolate_delta_histogram(0.90, 0.99) | bucket by method to 5m using interpolate_cumulative_histogram(rate, 0.90, 0.99) ``` ### Prometheus compatibility ``` | align to 5m using prom::rate // Prometheus-style rate ``` ## Identifiers Use backticks for names with special characters: ``my-dataset``, ``service.name``, ``http.request.duration`` # Examples Basic query: `my-metrics`:`http.server.duration` | align to 5m using avg Filtered: `my-metrics`:`http.server.duration` | where `service.name` == "frontend" | align to 5m using avg Grouped: `my-metrics`:`http.server.duration` | align to 5m using avg | group by endpoint using sum Rate: `my-metrics`:`http.requests.total` | align to 5m using prom::rate | group by method, path, code using sum Error rate (compute): ( `my-metrics`:`http.requests.total` | where code >= 400 | group by method, path using sum, `my-metrics`:`http.requests.total` | group by method, path using sum; ) | compute error_rate using / | align to 5m using avg SLI (error budget): ( `my-metrics`:`http.requests.total` | where code >= 500 | align to 1h using prom::rate | group using sum, `my-metrics`:`http.requests.total` | align to 1h using prom::rate | group using sum; ) | compute error_rate using / | map is::lt(0.2) | align to 7d using avg Histogram percentiles: `my-metrics`:`http.request.duration.seconds.bucket` | bucket by method, path to 5m using interpolate_delta_histogram(0.90, 0.99) Fill gaps: `my-metrics`:`cpu.usage` | map fill::prev | align to 1m using avgConnector
- Decline an RAI request — the requester is notified. Use when the artist does not want to authenticate the described work (wrong attribution, not by them, declining to confirm). TRIGGER: "decline that request," "reject the request from [name]," "that's not my work." Find the request_id via list_rai_requests — never ask the user for it. Confirm the decision before calling — this sends an email.Connector
- List endorsements the current account has received as an artist — galleries, dealers, or institutions that have countersigned your roster or specific works. Includes revoked (filter on revoked_at for currently active). TRIGGER: "who has endorsed me," "show my endorsements," "which galleries vouch for my work."Connector
- Returns a summary of all Carbone capabilities: supported formats, features, tool usage examples, and links to full documentation. Call this first if you are unsure what Carbone can do.Connector
- Use this tool to discover what has been saved in memory — e.g. at the start of a session, or when the user asks 'what have you saved?' or 'show me my memories'. Returns all saved memory keys with their preview, save date, and expiry. Optionally filter by a prefix (e.g. 'project-' to list only project memories). Pair with recall_memory to fetch the full content of any key.Connector
- Classify an AI system under EU AI Act 2024/1689 and return its risk tier, legal obligations, and compliance deadlines. Use this tool when: - An agent needs to assess whether an AI system is legally permitted in the EU - A company is building or deploying AI and needs to understand its regulatory obligations - You need to identify prohibited AI practices (real-time biometric surveillance, social scoring, etc.) - You need to know applicable CISA alerts and cybersecurity requirements for AI systems Returns: risk_tier (prohibited/high-risk/limited-risk/minimal-risk), applicable_articles, legal_obligations, compliance_deadline, CISA_alerts, and recommended_actions. Example call: checkAiCompliance({ company: "Acme Corp", system: "Facial Recognition Attendance System", description: "Real-time facial recognition used to track employee attendance in a factory" }) Cost: $0.005 USDC per call.Connector
- Create a new place — studio, gallery, museum, storage facility, or other location. TRIGGER: "my studio in Bushwick," "stored at," "I keep it at," or any mention of a specific location for works. Places are optional — only create when the user mentions locations. Include city/country when known.Connector
- Add a new slide to an existing presentation. Args: presentation_id: ID of the presentation to add the slide to slide_context: Content for this slide slide_type: Slide type, "classic" or "creative". Defaults to "classic". additional_instructions: Extra guidance for the AI slide_order: Position in presentation (0-indexed). Omit to append at end. Returns a generation_id to poll for completion.Connector
- Is AgentMarketSignal working? Check the real-time status of all 5 AI data pipelines (whale tracking, technical analysis, derivatives, narrative sentiment, market data) and the signal fusion engine. Returns last run times, durations, and any errors.Connector
- Lists stream objects in a given stream. * Parent parameter is in the form 'projects/{project name}/locations/{location}/streams/{stream name}', for example: 'projects/my-project/locations/us-central1/streams/my-stream'. * Not all the details of the stream objects are returned. * To get the full details of a specific stream object, use the 'get_stream_object' tool.Connector
- Lists the free capabilities available without an API key and explains how to get started. Call this on first connection to see what you can do immediately. Returns 5 free capability slugs (email-validate, dns-lookup, json-repair, url-to-markdown, iban-validate) with descriptions, example inputs, and instructions for accessing the full registry of 271 paid capabilities. No API key required.Connector