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image_info ($0.01)

image_info

Image dimensions and properties without any ML. POST {"image": "https://..."} -> width, height, aspect ratio, format, file size, transparency. Costs $0.01 in USDC on Base — pass paymentTx or payerAddress to pay (call how_to_pay for details).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageNo
paymentTxNoTransaction hash of your USDC payment on Base (see how_to_pay)
payerAddressNoYour wallet address, to spend existing credit without a new payment

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses pricing ($0.01 USDC on Base), payment methods (paymentTx or payerAddress), and the returned fields. However, it omits error behavior, input format constraints beyond the URL example, and any side effects, making it adequate but not exhaustive.

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 contain all essential information: purpose, HTTP method, output fields, cost, and payment instructions. Every sentence earns its place, with no redundant or filler content.

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?

Given no output schema and no annotations, the description lists the return fields, which is crucial. It also covers the payment flow adequately. Minor gaps like error handling or supported formats are present but not critical for such a simple tool.

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 67%, with the image parameter lacking a description. The description compensates by showing an example POST body with a URL, clarifying that image expects a URL. It also explains that paymentTx and payerAddress are alternative payment methods, enriching the schema descriptions already present.

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 clearly states the tool provides 'image dimensions and properties' and lists specific output fields (width, height, aspect ratio, format, file size, transparency). The phrase 'without any ML' distinguishes it from ML-based siblings like caption or image_palette, making its purpose unmistakable.

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 implies usage for basic image information extraction via 'without any ML', which sets it apart from ML-heavy alternatives. It does not explicitly name alternatives or state when not to use it, but the context is clear. The pointer to how_to_pay for payment details also guides users on a prerequisite step.

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

A3.6/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed descriptions, but a few potential confusions exist: image_resize vs image_transform overlap in image manipulation, and md (HTML-to-Markdown) vs markdown_to_html are easily mistaken. Overall, however, the vast majority of tools are unambiguous.

Naming Consistency4/5

The naming convention is predominantly snake_case with a verb_noun or object_verb pattern (e.g., address_validate, json_format, wallet_balance). A few single-word names like base64, pdf, and md break the pattern, but the style remains consistent enough to be predictable.

Tool Count2/5

With 68 tools, this server is far beyond the recommended range for a coherent MCP server. Even for a general-purpose utility API, the sheer number forces agents to wade through an unwieldy list, making selection expensive and error-prone.

Completeness4/5

For a general utility toolkit, the coverage is broad: conversions, validations, image processing, web/data extraction, and blockchain functions are all present. Minor gaps exist (e.g., URL encoding/decoding, more advanced string manipulation), but agents can work around them without dead ends.