Transcript for a YouTube Short — rejects long-form videos (≤3 min only). Costs 1 credit. Empty results and failures are never charged. Pass cache=true for a free 24h cache hit (default always fresh).
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602,772 tools. Updated 2026-09-23 14:09
"Understanding Cache for Prompt Engineering" matching MCP tools:
- Keyword search of public LinkedIn posts — offset cursor, ceiling 50. Costs ~16 credits (0.8/result). Empty results and failures are never charged. Pass cache=true for a free 24h cache hit (default always fresh).ConnectorNo auth
- Execute point-in-time queries for one or more engineering metrics. Returns current metric values for specified time periods, with support for batch queries and optional period-over-period comparisons. Time range (startTime/endTime) cannot exceed 6 months (180 days). PREREQUISITES - Follow this workflow: 1. Discover all available metrics ONCE: Call listMetricDefinitions (view='basic') - cache this response 2. Get metric query metadata ONCE per metric: Call listMetricDefinitions (view='full', key=METRIC_KEY) - supportedAggregations: Valid aggregation methods - orderByAttribute: Attribute path for sorting by metric values - groupByOptions[].key: Valid groupBy keys (use exact values, do NOT guess) - filterOptions[].key: Valid filter keys (use exact values, do NOT guess) Cache the full view response for each metric. Reuse the metadata from cached responses for subsequent queries on the same metric. 3. Construct query: Use the query metadata from the full view responses in step 2 to build valid point-in-time requests IMPORTANT: Cache only results from listMetricDefinitions. Do NOT cache point-in-time query results - always execute fresh queries for current data. Only refresh cached listMetricDefinitions responses if no longer in your context window or explicitly requested. Do NOT guess attribute names - always use exact values from listMetricDefinitions responses. Response includes: - Lightweight metadata: Column definitions optimized for programmatic use - Row data: Actual metric values and dimensional data - No heavy schemas: Source definitions excluded (get from listMetricDefinitions instead) Error responses: - 400: Invalid metric names, date range, validation errors, or unsupported metric combinations - 403: Feature not enabled (contact help@cortex.io)ConnectorNo auth
- One call, pick your field groups — resolves a slug OR any identifier and returns exactly the groups you ask for, instead of chaining get_provider + get_provider_rating + get_provider_artifacts + get_provider_onboarding. Groups: profile, onboarding, artifacts, rating, insights. Understanding plan — the base groups moved with the rest of the discovery layer on 2026-08-31. Priced B2 (cross-catalog synthesis) — $0.05 per call under pay-as-you-go; included in Understanding and Influence. See apis://prices.ConnectorNo auth
- Keyword pin search — offset cursor, SERP window 40. Costs ~13 credits (0.5/result). Empty results and failures are never charged. Pass cache=true for a free 24h cache hit (default always fresh).ConnectorNo auth
- Any text → crisp bullet points — Compress up to 16K characters of anything — articles, transcripts, email threads, reports — into 3-5 precise bullet points, one micro-payment per call. No API key, no subscription, no prompt engineering: send text=, get bullets back as clean JSON. The digest step for agent pipelines that read more than they can carry in context. Required input: text. Priced $0.03 per call over x402 on Base; send a prepaid x-credit-token header for unlimited calls, or get 1 free call/day per tool. No wallet or API key required.ConnectorNo auth
Matching MCP Servers
- AlicenseAqualityCmaintenanceA minimal MCP server that exposes Gemini's web and multimodal understanding (video, audio, document, image) through five dedicated tools.51MIT
- AlicenseNot gradedqualityBmaintenanceMCP server for multimodal understanding and object grounding (bounding boxes) across images, videos, and documents, with support for multiple AI providers (Zhipu GLM-V, OpenAI GPT-4o, Anthropic Claude, or any OpenAI-compatible endpoint).1MIT
Matching MCP Connectors
LLM caching proxy (x402 USDC on Base) - exact + semantic cache. Free health.
27 engineering compliance and calculation tools for the built environment (UK, EU, UAE).
- Laurent Knauss' technical skills, grouped by domain (Agentic AI, RAG & Voice AI, Software engineering & Cloud, Automation & tooling). Each skill has a label and an optional short detail. Use this to assess fit for AI/agentic development roles.ConnectorNo auth
- Calculate multi-provider LLM API inference token costs, prompt caching economics (up to 95% discount), batch API savings (50%), and cross-model cost disparity multipliers across frontier and high-efficiency models (Anthropic Claude, OpenAI GPT, Google Gemini, DeepSeek). Behavior: Deterministic, idempotent calculation with zero external side effects. Models official public provider pricing cards per million input/output tokens. Incorporates prompt cache hit pricing reductions and asynchronous batch API discounts. Evaluates real-time pack age and freshness status (FRESH < 14 days, AGING 14-30 days, STALE > 30 days). Returns comprehensive model cost matrix, cheapest and most expensive model arbitrage analysis, cache savings, and monthly cost projections. Usage Guidelines: Use when budgeting AI agent inference costs, evaluating LLM providers, or deciding whether to implement prompt caching or batch inference. Do not use for cloud network egress; use cloud_egress_finops instead.ConnectorNo auth
- Get aggregate statistics about missions on the HomeVisto platform. Returns total counts, status breakdown, and average bounty information. Useful for understanding platform activity.ConnectorNo auth
- Clear both the backend API cache and the solver oracle cache to force fresh fetches on next requests. Reports the number of entries cleared per cache.ConnectorNo auth
- Check subscription status, plan details, billing cycle, and feature access. Useful for understanding what the business can and cannot do on their current plan.ConnectorNo auth
- Classic Instagram embed snippet — blockquote.instagram-media + embed.js. Drop html into your page. Costs 1 credit. Empty results and failures are never charged. Pass cache=true for a free 24h cache hit (default always fresh).ConnectorNo auth
- Latest Facebook page Reels — views, likes, comments, shares; newest-first without archive padding. Costs 2 credits. Empty results and failures are never charged. Pass cache=true for a free 24h cache hit (default always fresh).ConnectorNo auth
- Get live activity counters — whale moves today, smart-money wallets active, golden alerts (10s cache) — Live activity snapshot for the platform (real recent counts, no fabricated floors). Cached ~10s. Response includes `meta.updatedAt` and `meta.cacheAgeSeconds` (derived from the underlying whale-copy-signals cache timestamp; 0 when the cache is cold).ConnectorNo auth
- Convert any number to scientific notation and engineering notation. Returns the coefficient, exponent, a formatted string with Unicode superscripts (e.g. '3.14 × 10²'), and engineering notation where the exponent is a multiple of 3. Useful for expressing very large or very small values compactly, common in physics, electronics (picofarads, gigahertz), and astronomy. Accepts output from sig_figs and log_calc for precision-aware formatting.ConnectorNo auth
- List the curated topic hubs (Claude Code, MCP, AI Search, LLM Engineering, AI Coding Agents) with their URLs.ConnectorNo auth
- Load all PROMPT IOCs from SpacetimeDB into the in-memory hash set. Call once at startup (or after a major feed update) to populate the sub-1ms query cache. Subsequent check_prompt() calls require no network access. The cache auto-refreshes every 5 minutes in the background. Returns: loaded: Number of PROMPT IOC hashes loaded duration_ms: Time taken to warm the cache window_sizes: Token window sizes used for queryingConnectorNo auth
- Check a prompt or text fragment for known PROMPT IOC patterns. Uses an in-memory hash set for sub-1ms token-level querying — no network calls after the cache is warmed. Slides a window of 3, 5, 8, and 10 tokens across the input and checks each window's canonical SHA256 against the PROMPT IOC feed. This is the primary real-time prompt injection detection endpoint. Call it on every user-supplied prompt before passing to the LLM. Args: text: The prompt text to check (raw, any length) auto_warm: If True and cache is empty, warm it first (adds ~300ms on first call only). Default True. Returns: matched: True if a known PROMPT IOC pattern was detected matched_hash: SHA256 of the matching token window (if matched) window_text: The matched token window text (if matched) window_size: Number of tokens in the matching window token_offset: Position in the token stream where match starts latency_us: Query latency in microseconds cache_size: Number of PROMPT IOC hashes currently cachedConnectorNo auth
- Return the Bicycle Cooperative membership offer for Kanbai: registry with the Software Engineering chapter substituted, option gates (interest vs rent) resolved, and this front's /join URL.ConnectorNo auth
- Get competitive ranking data in AI search results. Shows where the brand ranks relative to competitors across all tracked prompts and AI models. Useful for understanding competitive positioning. Views: - 'overall': Aggregate rankings across all prompts. - 'by-prompt': Rankings broken down per prompt. - 'prompt-trends': Daily average position per prompt, for the brand and the competitors it meets on that prompt. This is the view that answers which specific prompts are being lost and since when. Position is average rank in the answer, so LOWER is better and a positive `position_change` means the brand slipped down. Args: brand_id: The brand to check (required). view: 'overall', 'by-prompt' or 'prompt-trends'. Default 'overall'. days: Lookback window, 1-365. Default 30. view='overall' reads at most 90 days and reports what it used in days_effective; view='prompt-trends' uses at least 7 and at most 90. period: A frozen calendar period (YYYY-MM or YYYY-Qn). When given, days is ignored and the response carries a `period` block. view='overall' only; not found if no rank was archived for that period; win_rate and threat_count come back null. include_volume: Include search volume estimates. tag_ids: Optional. Comma-separated tag IDs from list_tags. Applies to view='overall' and view='prompt-trends'. limit: 'prompt-trends' only: how many prompts to return, most-measured first. 1-100, default 25.ConnectorOAuth
- FREE. List every Forward Deployed Engineering rule, playbook, and checklist phase available from this server — id, title, phase, and tags, with the paid body withheld. Call this first to see what is on offer before paying for anything.ConnectorNo auth