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image_pipeline

Run a multi-step image pipeline

Chain multiple operations (resize, compress, convert, crop) in sequence. The image is fetched once, then each operation is applied to the output of the previous one. Max 10 operations per pipeline.

Responses:

200: Processed image binary (Success Response) Content-Type: application/json Content-Type: image/jpeg

Example Response:

"string"

Content-Type: image/png

Example Response:

"string"

Content-Type: image/webp

Example Response:

"string"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYesImage source: a public URL (https://...) or a base64-encoded string (optionally as a data URI like data:image/png;base64,...).
deliveryNo
operationsYesOrdered list of operations to apply sequentially. Each operation receives the output of the previous one. Max 10.

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of disclosure. It explains the sequential chaining and max operations, but omits significant side effects: the delivery parameter can write bytes to a presigned PUT URL or POST to a callback URL. The response section only mentions 'Processed image binary,' potentially misleading the agent into thinking inline delivery is always used. This is a notable transparency gap.

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

Conciseness3/5

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

The core description is concise and front-loaded with a clear purpose. However, the response section is bloated with repeated 'Content-Type' and 'Example Response' lines that are identical and add confusion rather than value. This redundancy detracts from the overall conciseness.

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

Completeness3/5

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

The tool has a complex schema, no annotations, and no output schema. The description covers the pipeline concept and limit, but does not explain the delivery mode variations or how the return value differs when delivery is not inline. It also lacks error handling or authentication context. Given the complexity, this description is minimally viable but leaves important gaps.

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

Parameters2/5

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

Schema description coverage is 67%, so the description should compensate. However, it adds no parameter-specific meaning beyond what the schema already provides. The 'Max 10 operations' constraint is already present in the operations parameter description. The 'image is fetched once' note is a behavioral trait, not a parameter semantic. The delivery parameter is completely absent from the description, relying entirely on the schema.

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 starts with 'Run a multi-step image pipeline' and details 'Chain multiple operations (resize, compress, convert, crop) in sequence.' This clearly identifies the tool's specific function of combining image operations, distinguishing it from single-operation siblings like resize_image and compress_image.

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 provides clear context for when to use this tool: when multiple operations need to be chained. It states the image is fetched once and each operation applies to the previous output, plus the 10-operation limit. It does not explicitly name alternatives or exclusion criteria, but the multi-step versus single-step distinction is implied by the sibling tool names.

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