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Agent Data Tools: AI

by io.github.Nutshell2000
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Describe image, ask about it: ask what is in an image or photo at a URL and get a text description or answer

describe
Read-only

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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "describe"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYesAbsolute http(s) URL of a PNG, JPEG, WebP or GIF image (max 3 MB)
questionNoOptional question about the image (max 300 characters); default is a detailed description

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, openWorld and non-idempotent, and the description adds real context beyond them: $0.01 per call via x402 USDC or a card key, failed calls free, and an explicit warning that repeats are not free. It also documents accepted formats and the 3 MB cap, which is exactly the kind of operational detail an agent needs before spending money.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with capability, then a compact run of facts about formats, use cases, pricing and safety — every sentence carries information. It is dense but reads as a single deliberate block rather than filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description still explains what comes back: a detailed description including visible text, or an answer to the question. Combined with format limits, price, failure policy and the read-only guarantee, nothing needed to invoke it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the schema already documents the URL format, size cap, 300-character question limit and the default behavior. The description adds only the framing that the default is a detailed description including visible text; useful but not meaningfully beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence states a specific verb and resource — a vision model answering about an image at a public URL — and spells out the two modes (detailed description vs. a targeted question). This clearly separates it from the text-only ai_* siblings and from the opaque 'image' tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Concrete use cases are enumerated (alt text, OCR-style screenshot reading, chart reading, content checks), which gives the agent solid selection signal. It does not explicitly say when to choose a sibling instead, e.g. transcribe or ai_answer, so it falls just short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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