Agent Data Tools: AI
mcp remote · streamable-http · v1.82.9 · source: crawl
https://agent-data-tools.revmesh2074.workers.dev/mcp/ai
https://github.com/Nutshell2000/agent-data-tools
25 tools: LLM chat, summaries, translation, embeddings, speech, images. 25 free calls.
Works
90
Verified Oct 31, 2026 · p50 1094 ms · p95 1842 ms
Worth
no data
No outcomes reported yet. Agents report through report_outcome after a find_agents call.
Checks
| Check | Weight | Result | Evidence |
|---|---|---|---|
| reachable | 20 | pass | 3 of 3 handshakes succeeded; server 'agent-data-tools' |
| tools_listed | 15 | pass | 25 tools |
| schema_complete | 15 | pass | 25 of 25 tools have a description and a typed schema |
| latency_p50 | 10 | pass | p50 1094 ms |
| latency_p95 | 5 | pass | p95 1842 ms |
| error_free | 10 | pass | 0 of 3 attempts failed |
| auth_posture | 10 | pass | https, open |
| injection_scan | 10 | fail | ai_chat: references the system prompt; ai_chat_large: references the system prompt; ai_speech: base64 payload; image: base64 payload; ai_model_qwen3_30b: references the system prompt; ai_model_gpt_oss_20b: references the system prompt; ai_model_gpt_oss_120b: references the system prompt; ai_model_mistral_small: references the system prompt; ai_model_qwen_coder: references the system prompt; ai_model_deepseek_r1: references the system prompt |
| scope | 5 | pass | no execution-style tools |
Unknown counts as fail; n/a is left out of the denominator. How the weights work.
Tools (25)
| Name | Description | Schema |
|---|---|---|
ai_chat | Send a prompt, get an answer from Meta's Llama 4 Scout (17B active, 16 experts), with an optional system prompt, up to 1,024 output tokens and adjustable temperature. Fast and cheap. Use for drafting, | typed |
ai_chat_large | Send a prompt, get an answer from a larger open model: Llama 3.3 70B Instruct (default) or OpenAI gpt-oss-120b, with an optional system prompt, up to 1,024 output tokens and adjustable temperature. Sa | typed |
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. Example: text=pay-per | typed |
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 | typed |
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, | typed |
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. Price | typed |
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 | typed |
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 | typed |
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). Pr | typed |
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 | typed |
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 | typed |
ai_answer | Answer a question from the live web in one call: a Brave web search, the top result pages read, and an answer written by Llama 4 Scout from those sources only, with inline [n] citations and the source | typed |
ai_fact_check | Check a claim against the live web in one call: a Brave search, the top pages read, and a verdict from Llama 4 Scout using those sources only (supported, contradicted, mixed or unverified) with a shor | typed |
ai_rerank | Order up to 50 documents by how well each answers a query, with a relevance score from 0 to 1 (BAAI bge-reranker-base, a cross-encoder). Send documents as a JSON array or one per line; each is read up | typed |
ai_moderation | Classify a text as safe or unsafe with Meta's Llama Guard 3 and name the hazard categories it falls into (violent and non-violent crimes, sexual content, child exploitation, hate, self-harm, weapons, | typed |
ai_model_qwen3_30b | Chat with Alibaba's Qwen3 30B-A3B mixture-of-experts model (Apache 2.0) through one paid HTTP call: send a prompt and an optional system prompt, get the answer, up to 1,024 output tokens; its reasonin | typed |
ai_model_gpt_oss_20b | Chat with OpenAI's open-weight gpt-oss-20b reasoning model (Apache 2.0) through one paid HTTP call: send a prompt and an optional system prompt, get the answer, up to 1,024 output tokens. Also reachab | typed |
ai_model_gpt_oss_120b | Chat with OpenAI's open-weight gpt-oss-120b reasoning model (Apache 2.0) through one paid HTTP call: send a prompt and an optional system prompt, get the answer, up to 1,024 output tokens. Also reacha | typed |
ai_model_mistral_small | Chat with Mistral AI's Mistral Small 3.1 24B model (Apache 2.0) through one paid HTTP call: send a prompt and an optional system prompt, get the answer, up to 1,024 output tokens. Also reachable as mo | typed |
ai_model_qwen_coder | Chat with Alibaba's Qwen2.5 Coder 32B code model (Apache 2.0) through one paid HTTP call: send a prompt and an optional system prompt, get the answer, up to 1,024 output tokens. Also reachable as mode | typed |
ai_model_deepseek_r1 | Chat with DeepSeek's R1 reasoning model distilled into Qwen 32B (MIT) through one paid HTTP call: send a prompt and an optional system prompt, get the answer, up to 2,048 output tokens; its reasoning | typed |
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. Example: text=Th | typed |
ai_keywords | The most important keywords and key phrases of a text, most relevant first. Use for tagging, SEO, search indexing and topic detection. Price $0.01/call via x402 USDC or a card key (/credits), first 25 | typed |
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. | typed |
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. Example: text=How small busin | typed |
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