# Agent Data Tools: AI (remote · agent-data-tools.revmesh2074.workers.dev)

15 tools: LLM chat, summaries, translation, embeddings, speech, images. From $0.002.

- Trust score: 69/100 (medium)
- Registry status: active
- Liveness: live
- Owner verified: yes
- Last scored: 2026-10-24

## Components

- remote · `agent-data-tools.revmesh2074.workers.dev`: 69/100 (this document), [markdown](https://verifymcp.io/servers/nutshell2000-agent-data-tools-ai/mcp-ai.md), [page](https://verifymcp.io/servers/nutshell2000-agent-data-tools-ai/mcp-ai)

## Channel facts

- Endpoint: `https://agent-data-tools.revmesh2074.workers.dev/mcp/ai`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.53.0`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-10-24.

- **Endpoint Security**: 69/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one.
  - HTTPS enforcement could not be verified: the plaintext port answered with HTTP 405, which proves neither a plaintext path nor enforcement.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 68/100
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 2835 tokens (~189/item across 15 items; 15 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 17/100
  - Stability observed for 5 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Tool Safety**: 75/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - 0 of 2 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation; "ai_chat" implies "send" and declares readOnlyHint instead, contradicting what its own name says it does.
  - An AI judge read all 16 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### How do I install the Agent Data Tools: AI MCP server?

Agent Data Tools: AI is a hosted endpoint at https://agent-data-tools.revmesh2074.workers.dev/mcp/ai, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add --transport http nutshell2000-agent-data-tools-ai 'https://agent-data-tools.revmesh2074.workers.dev/mcp/ai'
```

### Cursor

```json
{
  "mcpServers": {
    "nutshell2000-agent-data-tools-ai": {
      "url": "https://agent-data-tools.revmesh2074.workers.dev/mcp/ai"
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "nutshell2000-agent-data-tools-ai": {
      "type": "http",
      "url": "https://agent-data-tools.revmesh2074.workers.dev/mcp/ai"
    }
  }
}
```

### Codex

```toml
[mcp_servers.nutshell2000-agent-data-tools-ai]
url = "https://agent-data-tools.revmesh2074.workers.dev/mcp/ai"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "nutshell2000-agent-data-tools-ai": {
      "type": "remote",
      "url": "https://agent-data-tools.revmesh2074.workers.dev/mcp/ai",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add nutshell2000-agent-data-tools-ai --url 'https://agent-data-tools.revmesh2074.workers.dev/mcp/ai' --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  nutshell2000-agent-data-tools-ai:
    url: "https://agent-data-tools.revmesh2074.workers.dev/mcp/ai"
```

### Netclaw

```json
{
  "McpServers": {
    "nutshell2000-agent-data-tools-ai": {
      "Transport": "http",
      "Url": "https://agent-data-tools.revmesh2074.workers.dev/mcp/ai"
    }
  }
}
```

### Vellum

```bash
assistant mcp add nutshell2000-agent-data-tools-ai -t streamable-http -u 'https://agent-data-tools.revmesh2074.workers.dev/mcp/ai'
```

### Other

```json
{
  "mcpServers": {
    "nutshell2000-agent-data-tools-ai": {
      "type": "http",
      "url": "https://agent-data-tools.revmesh2074.workers.dev/mcp/ai"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-10-24 (score 69, +1)

No change was recorded against any check on this day. Stability & Change Management went from 13 to 17. That category is still filling its 30-day observation window: 4 days of observed history at the previous scan, 5 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-23 (score 68, 0)

- [security] Tool “ai_chat” rewrote its description, which is the text the model reads
- [functional] Server version: 1.51.3 → 1.52.0
- [functional] Server version: 1.50.5 → 1.51.3

### 2026-10-22 (score 68, +1)

- [security] The server rewrote its instructions, which are the text every model session reads
- [security] Tool “ai_chat” rewrote its description, which is the text the model reads
- [security] Tool “ai_classify” rewrote its description, which is the text the model reads
- [security] Tool “ai_embed” rewrote its description, which is the text the model reads
- [security] Tool “ai_extract_json” rewrote its description, which is the text the model reads
- [security] Tool “ai_keywords” rewrote its description, which is the text the model reads
- [security] Tool “ai_outline” rewrote its description, which is the text the model reads
- [security] Tool “ai_rewrite” rewrote its description, which is the text the model reads
- [security] Tool “ai_sentiment” rewrote its description, which is the text the model reads
- [security] Tool “ai_speech” rewrote its description, which is the text the model reads
- [security] Tool “ai_summarize_text” rewrote its description, which is the text the model reads
- [security] Tool “ai_translate” rewrote its description, which is the text the model reads
- [functional] Server version: 1.50.3 → 1.50.5
- [functional] Server version: 1.50.2 → 1.50.3
- [functional] Server version: 1.50.1 → 1.50.2
- [functional] Server version: 1.49.1 → 1.50.1
- [functional] Server version: 1.48.6 → 1.49.1
- [functional] Server version: 1.48.4 → 1.48.6
- [functional] Server version: 1.48.2 → 1.48.4
- [functional] Server version: 1.48.0 → 1.48.2
- [functional] Server version: 1.47.15 → 1.48.0
- [functional] Server version: 1.47.7 → 1.47.15
- [functional] Server version: 1.45.3 → 1.47.7
- [cosmetic] Tool “ai_classify” changed its title: Classify text into your labels → Text classification into labels
- [cosmetic] Tool “ai_extract_json” changed its title: Extract fields from text as JSON → Extract JSON fields from text
- [cosmetic] Tool “ai_keywords” changed its title: Extract keywords → Keyword extraction
- [cosmetic] Tool “ai_chat” changed its title: LLM chat completion: Llama 3.1 8B → LLM chat completions: Llama 3.1 8B
- [cosmetic] Tool “ai_embed” changed its title: Text embeddings (BGE-M3, 1,024 dimensions) → Embeddings, OpenAI compatible: BGE-M3, 1,024 dimensions
- [cosmetic] Tool “ai_chat” changed its title: Chat completion (Llama 3.1 8B) → LLM chat completion: Llama 3.1 8B

### 2026-10-21 (score 67, 0)

- [security] Tool “ai_chat” rewrote its description, which is the text the model reads
- [functional] Server version: 1.45.1 → 1.45.3
- [functional] Server version: 1.45.0 → 1.45.1
- [functional] Server version: 1.44.0 → 1.45.0
- [cosmetic] Tool “ai_embed” changed its title: Text embeddings (BGE-M3) → Text embeddings (BGE-M3, 1,024 dimensions)
- [cosmetic] Tool “ai_sentiment” changed its title: Sentiment of text → Sentiment analysis of text
- [cosmetic] Tool “ai_speech” changed its title: Text to speech (audio file) → Text to speech (TTS): turn text into an audio file
- [cosmetic] Tool “image” changed its title: Generate an image from a text prompt → Image generation from a text prompt
- [cosmetic] Tool “transcribe” changed its title: Speech to text for an audio file → Transcribe audio: speech to text for an audio file

### 2026-10-20 (score 67, 0)

- [security] The server rewrote its instructions, which are the text every model session reads
- [security] Tool “ai_chat” rewrote its description, which is the text the model reads
- [security] Tool “ai_chat_large” rewrote its description, which is the text the model reads
- [security] Tool “ai_outline” rewrote its description, which is the text the model reads
- [security] Tool “ai_rewrite” rewrote its description, which is the text the model reads
- [security] Tool “ai_speech” rewrote its description, which is the text the model reads
- [security] Tool “ai_translate” rewrote its description, which is the text the model reads
- [security] Tool “image” rewrote its description, which is the text the model reads
- [functional regression] Schema quality: 2409 → 2829
- [functional improvement] Stability: unverified → 0.03
- [functional] Server version: 1.41.0 → 1.44.0
- [functional] Server version: 1.40.0 → 1.41.0
- [functional] Server version: 1.39.3 → 1.40.0
- [functional] Server version: 1.37.1 → 1.39.3
- [functional] Server version: 1.36.1 → 1.37.1
- [functional] Server version: 1.35.3 → 1.36.1
- [functional] New tool “ai_chat_large”

### 2026-10-19 (score 67)

First indexed and scored.

## MCP tools (15)

### `image` (~163 tokens)

Image generation from a text prompt

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. Example: prompt=a lighthouse on a cliff at sunset, watercolor. Price $0.02/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input parameters:

- `prompt` (string, required): What the image should show (3-800 characters)
- `steps` (integer): Diffusion steps, 1-8 (default 4)

### `transcribe` (~163 tokens)

Transcribe audio: speech to text for an audio file

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. Example: url=https://commons.wikimedia.org/wiki/Special:FilePath/En-us-hello.ogg. Price $0.02/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input parameters:

- `url` (string, required): Absolute http(s) URL of an audio file (max 5 MB)

### `describe` (~164 tokens)

Describe or question an image

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. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input parameters:

- `question` (string): Optional question about the image (max 300 characters); default is a detailed description
- `url` (string, required): Absolute http(s) URL of a PNG, JPEG, WebP or GIF image (max 3 MB)

### `ai_chat` (~203 tokens)

LLM chat completions: Llama 3.1 8B

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. For harder reasoning use ai_chat_large. Example: prompt=Explain x402 in one sentence.. Price $0.002/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input parameters:

- `maxTokens` (integer): Maximum output tokens, 1-1024 (default 400)
- `prompt` (string, required): User prompt, up to 8,000 characters
- `system` (string): Optional system prompt, up to 2,000 characters
- `temperature` (number): 0-1.5 (default 0.6)

### `ai_chat_large` (~248 tokens)

Chat completion (Llama 3.3 70B or gpt-oss-120b)

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. Same call as /ai/chat with a stronger model. Use when the 8B model is not enough: reasoning, drafting and Q&A. Example: prompt=Explain x402 in one sentence.. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input parameters:

- `maxTokens` (integer): Maximum output tokens, 1-1024 (default 400)
- `model` (string): llama-3.3-70b (default) or gpt-oss-120b
- `prompt` (string, required): User prompt, up to 8,000 characters
- `system` (string): Optional system prompt, up to 2,000 characters
- `temperature` (number): 0-1.5 (default 0.6)

### `ai_summarize_text` (~139 tokens)

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). Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input parameters:

- `sentences` (integer): Summary length in sentences, 1-8 (default 3)
- `text` (string, required): Text to summarize, up to 12,000 characters

### `ai_sentiment` (~123 tokens)

Sentiment analysis of text

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=The checkout was fast and support answered within minutes.. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input parameters:

- `text` (string, required): English text, up to 4,000 characters

### `ai_classify` (~125 tokens)

Text classification into labels

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 $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input parameters:

- `labels` (string, required): Comma-separated candidate labels (2-20)
- `text` (string, required): Text to classify, up to 8,000 characters

### `ai_keywords` (~111 tokens)

Keyword extraction

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); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input parameters:

- `count` (integer): How many, 3-30 (default 10)
- `text` (string, required): Text, up to 12,000 characters

### `ai_rewrite` (~149 tokens)

Rewrite text in a style

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. Example: text=hey, cant make it tmrw, can we move to thu?. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input parameters:

- `style` (string): formal, casual, simpler, shorter, friendly, professional or persuasive (default professional)
- `text` (string, required): Text, up to 8,000 characters

### `ai_outline` (~139 tokens)

Outline a text or topic

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 businesses can accept stablecoin payments. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input parameters:

- `depth` (integer): Heading levels, 1-3 (default 2)
- `text` (string, required): Text to outline, or a topic, up to 12,000 characters

### `ai_translate` (~167 tokens)

Translate text (100 languages)

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. Example: text=Payment received, thank you!, to=sv. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input parameters:

- `from` (string): Source language code (default en)
- `text` (string, required): Text, up to 4,000 characters
- `to` (string, required): Target language code, e.g. sv

### `ai_embed` (~130 tokens)

Embeddings, OpenAI compatible: BGE-M3, 1,024 dimensions

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-call APIs for AI agents. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input parameters:

- `text` (string, required): Text to embed, up to 4,000 characters

### `ai_extract_json` (~131 tokens)

Extract JSON fields from text

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. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input parameters:

- `fields` (string, required): Comma-separated field names to extract (1-30)
- `text` (string, required): Source text, up to 12,000 characters

### `ai_speech` (~163 tokens)

Text to speech (TTS): turn text into an audio file

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. Example: text=Your payment was received. Thank you!. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input parameters:

- `lang` (string): Language: en, es, fr, zh, ja or ko (default en)
- `text` (string, required): Text to speak, up to 1,000 characters

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/nutshell2000-agent-data-tools-ai/mcp-ai#diagnostics

## Score history

- 2026-10-24: 69
- 2026-10-23: 68
- 2026-10-22: 68
- 2026-10-21: 67
- 2026-10-20: 67
- 2026-10-19: 67

## Common questions

### What is the Agent Data Tools: AI MCP server?

Agent Data Tools: AI is an MCP server listed in the public MCP registry as io.github.Nutshell2000/agent-data-tools-ai. 15 tools: LLM chat, summaries, translation, embeddings, speech, images. From $0.002. This page covers its hosted endpoint (https://agent-data-tools.revmesh2074.workers.dev/mcp/ai). Its publisher has verified ownership with us.

### Is the Agent Data Tools: AI MCP server safe to use?

Agent Data Tools: AI scores 69 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the Agent Data Tools: AI MCP server expose?

Agent Data Tools: AI exposes 15 tools: image, transcribe, describe, ai_chat, ai_chat_large, and 10 more. Their descriptions and schemas cost roughly 2,318 tokens of context every time the server is loaded.

### Does the Agent Data Tools: AI MCP server require authentication?

No. We connected to Agent Data Tools: AI without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.

### Is the Agent Data Tools: AI MCP server still maintained?

Agent Data Tools: AI is still listed as active in the MCP registry. We last reached this channel on 24 October 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

## Links

- Remote endpoint: https://agent-data-tools.revmesh2074.workers.dev/mcp/ai
- Repository: https://github.com/Nutshell2000/agent-data-tools
- Website: https://agent-data-tools.revmesh2074.workers.dev/
- Changelog RSS feed: https://verifymcp.io/servers/nutshell2000-agent-data-tools-ai/mcp-ai.xml
- Changelog JSON feed: https://verifymcp.io/servers/nutshell2000-agent-data-tools-ai/mcp-ai.json
- HTML version of this page: https://verifymcp.io/servers/nutshell2000-agent-data-tools-ai/mcp-ai