io.github.Nutshell2000/agent-data-tools-ai · v1.28.0 · ai-and-media
Agent Data Tools: AI is healthy: it completed an MCP handshake in 2879 ms and exposes 14 tools.
14 tools: LLM chat, summaries, translation, embeddings, speech, images. From $0.002.
Endpoint: https://agent-data-tools.revmesh2074.workers.dev/mcp/ai (streamable-http)
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| Users (last 30 days) | 0 |
| Handshake | Healthy |
| Auth needed | None |
| Tool count | 14 |
| Latency | 2879 ms |
| Last checked (UTC) | 2026-10-19T09:46:03.999Z |
| Checked (UTC) | Status | Latency | Error |
|---|---|---|---|
| 2026-10-19T09:46:03.999Z | Healthy | 2879 |
image | Text-to-image generation with FLUX.1 schnell. Send a prompt, get a 1024x1024 JPEG back as base64 in a few seconds. Use for illustrations, thumbnails, mockups, avatars and social media images. Optional steps parameter (1-8, default 4) trades speed for detail. Side effects: none on any outside system |
transcribe | Transcribe speech from an audio file at a public URL using Whisper large-v3-turbo. Returns the transcript text, detected language, duration and word count. Accepts common formats such as MP3, WAV, M4A and OGG up to 5 MB. Use for voice notes, podcast clips, meeting snippets and voice messages. Side e |
describe | Vision model for an image at a public URL: get a detailed description including any visible text, or pass a question to ask something specific about the image. Accepts PNG, JPEG, WebP and GIF up to 3 MB. Use for alt text, OCR-style reading of screenshots, chart reading and content checks. Side effec |
ai_chat | Send a prompt, get an answer from Llama 3.1 8B Instruct, with an optional system prompt, up to 1,024 output tokens and adjustable temperature. Use for drafting, reasoning steps, Q&A and any quick LLM call without an AI provider account. Side effects: none on any outside system (it only reads or comp |
ai_summarize_text | Summarize up to 12,000 characters of text you send into a few sentences plus bullet key points. Use for long emails, documents, transcripts and tool outputs (for a web page by URL, use /summarize). Side effects: none on any outside system (it only reads or computes), but each successful call costs $ |
ai_sentiment | Positive or negative sentiment of English text with a confidence score, from a DistilBERT model fine-tuned on SST-2. Use to triage reviews, support messages, social posts and replies. Side effects: none on any outside system (it only reads or computes), but each successful call costs $0.002 in USDC |
ai_classify | Zero-shot classification: give text and 2-20 candidate labels, get the best label, a short reason and a ranking. Use for routing tickets, tagging content, intent detection and moderation queues. Side effects: none on any outside system (it only reads or computes), but each successful call costs $0.0 |
ai_keywords | The most important keywords and key phrases of a text, most relevant first. Use for tagging, SEO, search indexing and topic detection. Side effects: none on any outside system (it only reads or computes), but each successful call costs $0.002 in USDC on Base or another supported network, paid with x |
ai_rewrite | Rewrite text to be more formal, casual, simpler, shorter, friendlier, more professional or more persuasive, keeping the meaning and language. Use to polish emails, replies, product copy and messages. Side effects: none on any outside system (it only reads or computes), but each successful call costs |
ai_outline | A structured Markdown outline (headings and bullet points) of a text you send, or a suggested outline for a topic. Use to plan articles, reports, talks and documentation. Side effects: none on any outside system (it only reads or computes), but each successful call costs $0.002 in USDC on Base or an |
ai_translate | Translate text between about 100 languages with Meta's M2M100 1.2B model. Give the target language as an ISO code such as en, sv, de, fr, es, zh or ja; the source language is optional. Use to localise messages and documents. Side effects: none on any outside system (it only reads or computes), but e |
ai_embed | A 1,024-dimension embedding vector for up to 4,000 characters of text in 100+ languages, from BAAI's BGE-M3. Use for semantic search, RAG retrieval, clustering and deduplication. Side effects: none on any outside system (it only reads or computes), but each successful call costs $0.002 in USDC on Ba |
ai_extract_json | Pull named fields out of unstructured text (emails, invoices, listings, bios) and get them back as a JSON object, with null for anything not present. Use to turn messy text into structured data for an agent. Side effects: none on any outside system (it only reads or computes), but each successful ca |
ai_speech | Turn up to 1,000 characters of text into natural speech as an audio file (base64, with its content type), in English, Spanish, French, Chinese, Japanese or Korean, with MeloTTS. Use for voice replies, audio notes, accessibility and phone or chat bots. Side effects: none on any outside system (it onl |