Pictomancer.ai

estimate_cost

Estimate the price of an operation

Returns the exact USD price this API would charge for an operation on an input of the given size, without fetching or processing anything. Free. Use it before paying: send X-Max-Cost-USD on the real request to have the API refuse (412) instead of charging more than you allowed. The list price is returned even when the request could end up free (free tier, or a compress that does not shrink the file).

Responses:

200: Successful Response (Success Response) Content-Type: application/json

Example Response:

{
  "operation": "Operation",
  "price_usd": 1.0,
  "base_usd": 1.0,
  "surcharges_usd": {},
  "size_multiplier": 1.0,
  "within_free_tier": true,
  "free_tier_remaining": 1,
  "currency": "Currency",
  "network": "Network"
}

Input Schema

TableJSON Schema

Name	Required	Description
`format`	No	Requested output format; only avif changes the price.
`operation`	Yes	Operation to price. One of resize, compress, convert, crop, optimize_for_vision, optimize_generated, pipeline. analyze is always free.
`operations`	No	For pipeline only: the ordered operations, each {type, format?}. Volume discount applies at 3+ operations.
`input_bytes`	Yes	Size of the input image in bytes. Get it from analyze_image (free) if you do not know it.
`quality_target`	No	Whether the request will use quality_target (SSIM search surcharge).

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations provided, the description carries full burden. It clearly states no processing occurs, it's free, and explains behavior even when requests could be free (free tier, compress that doesn't shrink). This is transparent about key behaviors.

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?

The description is front-loaded with the core purpose and usage advice, followed by a response example. It is well-structured and each sentence adds value, though slightly longer than minimal.

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 5 parameters, no output schema, and no annotations, the description covers the essential points: pricing behavior, free tier, usage with max-cost header, and a detailed response example. It is complete for a cost estimation tool though could mention error cases.

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%, so baseline is 3. The tool description does not add extra meaning beyond the schema's parameter descriptions. It reiterates some context (e.g., 'analyze is always free' in operations) but primarily relies on schema, which is adequate.

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 explicitly states 'Estimate the price of an operation' and elaborates that it returns the exact USD price without fetching or processing anything. This clearly differentiates it from sibling tools that perform actual image operations.

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?

The description advises using the tool before paying and mentions the X-Max-Cost-USD header to cap charges, providing explicit when-to-use guidance. It lacks explicit when-not-to-use or alternatives, but the context is sufficient.

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

A4/5.0

Disambiguation4/5

Most tools have clearly distinct purposes (analyze, compress, convert, crop, resize, pipeline, optimize_for_vision, optimize_generated_image). However, there is some overlap between compress_image and convert_image (both deal with quality settings and can change format), and between resize_image and crop_image (resize's fill mode with smart-crop overlaps crop's smart crop). The pipeline tool could theoretically subsume any of the single-operation tools, which introduces a slight ambiguity in when to use pipeline vs. individual tools.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (analyze_image, compress_image, convert_image, crop_image, get_format_info, image_pipeline, optimize_for_vision, optimize_generated_image, resize_image). The naming is predictable and self-documenting, with no mixing of camelCase or other conventions.

Tool Count5/5

With 9 tools, the server is well-scoped for an image processing domain. Each tool covers a core operation (analyze, compress, convert, crop, resize, pipeline, format info, and two optimization tools). The count feels appropriate—not too few to limit usefulness, not too many to be overwhelming.

Completeness5/5

The tool set provides comprehensive coverage for common image manipulation tasks: analysis, compression, format conversion, cropping, resizing, optimization for both general and AI-generated images, and a pipeline for chaining operations. Missing features like rotation, flipping, or color adjustments are minor but the core CRUD-like operations (read/analyze, write/convert, resize/crop) are well-represented, and the pipeline tool mitigates gaps by allowing combinations.

Resources

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