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OCR or describe an image

image_to_text

Transcribe an image to text (OCR) or describe it via a vision model — steerable with your own prompt. Paid: call without x_payment to receive this call's exact terms (amount, asset, network), sign them, then call again with x_payment. The free pricing tool lists every price at once.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the image.
promptNoWhat to do with it; omit for a straight transcription.
x_paymentNoOptional signed x402 payment payload (base64, what the X-PAYMENT header carries). Omit to receive the exact payment terms; sign them (e.g. @x402/fetch) and call again with this argument to settle and get the data.
max_tokensNoOutput cap.

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the burden and discloses the paid nature and exact payment flow (call without x_payment to get terms, sign, call again with x_payment). It also notes that the prompt steers the model. It does not mention rate limits or auth details, but the payment behavior is the most critical behavioral trait and is well covered.

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

Conciseness5/5

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

Two sentences, front-loaded with the main purpose, then payment guidance. Every sentence earns its place with zero fluff.

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

Completeness4/5

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

The description covers the core functionality, payment flow, and alternative tool. It doesn't describe the return format or error behavior, but for a straightforward OCR/vision tool, the output is inherently clear from the purpose. No output schema exists, but the description is sufficient for an agent to select and invoke it.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the x_payment flow (omit to receive terms, include to settle) and clarifies that omitting prompt means straight transcription. This goes beyond the schema's parameter descriptions.

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 description opens with a specific verb-resource pair: 'Transcribe an image to text (OCR) or describe it via a vision model.' This clearly distinguishes it from sibling tools like pdf_extract (PDFs) and read_url (web pages).

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?

It gives clear context: use for OCR or vision-based description, and it explicitly points to the free `pricing` tool as an alternative for price discovery. It also explains the two-step payment workflow neatly. However, it doesn't explicitly state when not to use this tool versus other image-related tools (e.g., screenshot_url), so it loses one point.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool serves a clearly distinct function—data discovery, holidays, icons, OCR, news, PDF extraction, pricing, URL reading, data requests, screenshots, timezone, and weather. There is no overlap in purpose; even read_url and screenshot_url differ by output format (markdown vs PNG).

Naming Consistency4/5

All tool names use lowercase snake_case and are readable, but the pattern is not uniformly verb_noun—some are nouns (holidays, news, pricing, timezone, weather) while others are verb_noun (find_data, read_url). This is a minor inconsistency that doesn't impede predictability.

Tool Count5/5

With 12 tools, the server sits comfortably within the ideal 3–15 range. Each tool earns its place covering a distinct web utility, and the count matches the broad but well-defined scope of a general-purpose web toolbox.

Completeness4/5

The tool surface is fairly complete for a general-purpose web utility server, covering common tasks like fetching, extracting, searching, and checking time/weather/holidays, plus meta tools (pricing, request_data). Minor gaps like a generic text summarizer or video tool exist, but nothing critical for the intended purpose.

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