image-processing

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

With no annotations, the description carries the behavioral disclosure burden. It reveals useful traits: fetching from URL/base64, returning specific metadata, 'Always free', and omitting dimensions if the header cannot be read. This goes beyond the schema, though it doesn't mention other failure modes or authorization requirements.

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

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

The description is well-structured and front-loaded, with a clear first sentence and detailed follow-up. The 'Responses' section and example response add useful context, though the example is minimalistic and slightly inconsistent with the broader list of returned fields, preventing a perfect score.

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

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

The tool is simple (1 parameter, no output schema, no annotations), and the description adequately covers input formats, output fields, a special case (missing dimensions), and cost implications. It provides enough context for an agent to use the tool correctly, though a fuller example response would improve completeness.

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

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

Schema description coverage is 100% and the schema already fully describes the 'source' parameter as a URL or base64 string, optionally as a data URI. The description adds no new parameter semantics beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states the tool's action ('Analyze an image'), the input type (URL or base64), and the specific metadata returned (size, dimensions, format, token costs). It distinguishes itself from sibling tools that primarily modify or transform images (compress, convert, crop, resize, optimize).

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

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

The description implies when to use the tool—when you need image metadata or model token costs—but does not explicitly state when not to use it or directly compare against alternatives like get_format_info. The 'Always free' note hints at a benefit, but usage guidance is mostly implied rather than explicit.

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

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

With no annotations, the description carries the full burden. It does disclose billing behavior (free when output isn't smaller, X-Pig-Billed: 0), quality_target search with surcharge, and response output format. However, it omits significant side effects of the delivery parameter (writing to PUT URLs or POSTing callbacks) and contains an internal inconsistency: the initial supported-format list excludes avif, but later mentions avif for quality_target.

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

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

The core description is efficient, but the 'Responses' section is lengthy and repetitive, with three near-identical 'Example Response: string' blocks. This adds noise without providing useful information, detracting from overall conciseness.

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

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

The description covers key compression behavior, billing, and supported formats, but the tool is complex (7 params, delivery modes with side effects). It lacks guidance on delivery options and alternative tools, and the internal format inconsistency adds confusion. The schema fills some gaps, but the description alone is not fully complete.

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

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

Schema description coverage is high at 86%, so baseline is 3. The description adds meaningful detail beyond the schema by explaining quality_target's SSIM search and surcharge, and how it 'instead of' q. This goes above baseline.

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

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

The description opens with 'Compress an image' and clarifies 'Re-encode an image with quality/format options to reduce file size,' which clearly states the tool's purpose and distinguishes it from crop, resize, and analyze siblings. However, it does not explicitly contrast with convert_image, so it stops short of full sibling differentiation.

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

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

The description implies usage for reducing file size and explains the internal choice between q and quality_target, but it gives no explicit guidance on when to use compress_image vs alternatives like convert_image or optimize_for_vision. Context is clear, but exclusion criteria are absent.

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

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

With no annotations, the description carries the full burden and does a good job. It discloses optional behaviors (quality, strip metadata, lossless mode, effort) and explains the quality_target mechanism, including server-side search, surcharge, and resulting X-Pictomancer-Quality-* headers. It also indicates the response is a processed image binary. Missing details like authentication or rate limits, but these are not critical for this tool's operation.

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

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

The first paragraph is concise and informative, but the response section is bloated and repetitive: the same example response ('"string"') is shown three times with only a different Content-Type header, and 'Content-Type: application/json' appears inconsistently. This redundancy adds noise without value, harming structure and conciseness.

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

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

For a tool with 9 parameters and no output schema, the description is fairly complete. It explains the core conversion purpose, lists formats, covers quality_target, quality, strip, lossless, and effort, and outlines the HTTP response. The missing delivery-mode details and error cases are well covered in the schema, so the description does not need to repeat them. The messy response section is a minor detractor.

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

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

Schema description coverage is 89% (high), so the baseline is 3. The description adds some context beyond the schema by clarifying the trade-off between q and quality_target and mentioning the flat surcharge, but most parameter semantics (e.g., delivery modes, effort, autorot) are already well described in the schema. The description does not introduce meaning for the less-covered parameters.

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

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

The description opens with 'Convert image format' and immediately specifies the action: converting an image to a different format, listing all supported formats (jpeg, png, webp, tiff, gif, avif). This clearly distinguishes it from sibling tools focused on analysis, compression, cropping, resizing, and other image operations.

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

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

The usage is implied: use when you need to change an image's format. However, there is no explicit guidance on when not to use it or which alternative tool to choose for related operations (e.g., compress_image for size reduction without format change). The description does not exclude any scenarios or mention sibling tools.

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

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

With no annotations provided, the description carries the full burden. It discloses clamping behavior for smart crop, the X-Pictomancer-Trim-* headers for trim, and indicates the response is a processed image binary. It does not elaborate on delivery options or potential side effects, but these are covered by the schema and not required for basic usage.

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

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

The first paragraph is tight and front-loaded, but the Responses section is poorly structured with redundant Content-Type lines and a meaningless example ('string'). This reduces clarity and wastes space, making the overall description less concise than it could be.

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

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

The description covers the three modes, clamping, trim headers, and response type. With 11 parameters but 91% schema coverage, the additional context is sufficient for most users, though it omits delivery options (put_url/callback) from the prose, which are still documented in the schema.

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

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

Schema coverage is high (91%), so the baseline is 3. The description adds a conceptual framework by grouping parameters into three modes and explains how 'gravity' works and what 'threshold' affects, which goes beyond individual schema descriptions and adds real meaning.

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

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

The description opens with the verb 'Crop an image' and immediately explains the three mutually exclusive modes (Manual, Smart crop, Trim), clearly defining what the tool does. It distinguishes from sibling tools like resize_image or compress_image by focusing specifically on cropping and the modes.

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

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

The description explicitly tells which parameters to use for each of the three cropping modes and notes mutual exclusivity, providing clear context for when to use each mode. However, it does not explicitly direct users away to a sibling tool for non-crop operations, so it lacks explicit alternative exclusions.

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

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

With no annotations, the description carries the transparency burden. It clearly states that the tool returns supported formats and options, which implies a read-only query operation. The absence of side effects or side-channel behavior is adequately conveyed for a tool of this simplicity.

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

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

The description is short but somewhat redundant: 'Get supported formats and options' and 'Returns supported output formats and their configurable options' restate the same idea. The Responses section adds standard HTTP status detail, but the first two sentences could be merged into one efficient line.

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

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

For a simple, parameterless query tool, the description is reasonably complete. It states the purpose and the return value. Although there is no output schema, the description explains what is returned (supported formats and options). The sibling context clarifies that this is a non-action informational tool, making the overall picture adequate.

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

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

The tool accepts zero parameters, so the input schema provides no semantic content. The description correctly adds no parameter information because there is none to add. Per the rubric, a 0-parameter tool receives a baseline of 4.

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

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

The description clearly identifies the tool's function: it gets supported formats and returns their configurable options. The verb 'Get' and the specific resource 'supported formats and options' make the purpose unambiguous, and it is distinct from sibling tools that perform image transformations.

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

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

Usage context is implied: given the sibling tools all perform image operations, this is evidently a query/utility tool for retrieving format information. However, there is no explicit guidance on when to use it instead of alternatives, nor any mention of prerequisites or exclusions.

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

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 key behaviors: image is fetched once, operations apply sequentially, max 10 operations, and response content types. It omits side effects like writing to PUT URLs or callbacks, but covers essential processing behavior well.

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

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

The description is concise and front-loaded with the core behavior, followed by structured response examples. No wasted words; the separation of processing logic and responses aids readability.

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

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

The tool is complex with delivery modes and multi-step processing, and the description covers the pipeline flow and limits. Missing details like error cases and delivery side effects are partly covered by the schema, making the description adequate for selection and invocation.

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

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

Schema coverage is 67%, with detailed descriptions for source and operations. The description restates operation types but adds little beyond the schema. It does not clarify delivery semantics, which are complex but already well described in the schema.

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

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

Description clearly states 'Run a multi-step image pipeline' and details that it chains operations (resize, compress, convert, crop) in sequence. This distinguishes it from sibling single-operation tools 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.

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

The description implies use for multi-step workflows by explaining sequencing and a 10-operation limit. It doesn't explicitly contrast with single-operation siblings, but the use case is evident from the context.

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

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

With no annotations, the description carries the full burden and does well: it discloses that resizing alone saves bytes/latency but not tokens, that max_tokens behaves differently across Claude/OpenAI/Gemini, and that in-budget images are returned untouched and free (X-Pig-Billed: 0). It does not discuss rate limits or auth, but those are less central here.

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

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

The main paragraph is front-loaded and dense with meaningful info about provider-specific behavior and token economics. The Responses section is slightly redundant with repeated Content-Type headers and 'string' placeholders, but it does provide return-type hints in the absence of an output schema.

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

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

Given no output schema, the description explains the outcome (resized image, token cost report, free/billed header) and includes response examples. The schema covers the remaining parameter semantics well. Minor gap: it does not detail error cases beyond the schema's note on unknown model IDs.

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

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

Schema description coverage is 83% and parameter docs are already detailed (e.g., max_tokens and target_model). The description reinforces max_tokens behavior but adds little beyond the schema's own parameter descriptions, so the baseline of 3 is appropriate.

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

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

The description clearly states the tool resizes an image for a vision model and reports its token cost before and after. This specific verb+resource+scope distinguishes it from sibling tools like resize_image or compress_image, which do not include model-specific token reporting.

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

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

It provides clear context: use this when preparing an image for a vision model, and explains when to pass max_tokens to trade resolution for tokens. However, it does not explicitly name alternatives or state when not to use this tool versus resize_image or compress_image, so it lacks explicit exclusions.

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

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

With no annotations, the description carries the full burden. It discloses the response format ('Processed image binary' with content types), describes the smart-crop behavior in fill mode, and notes the gravity default. It does not mention delivery modes or source immutability, but those are documented in the schema.

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

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

The description is well-structured with a concise opening paragraph and a clearly separated response section. The response section is slightly repetitive listing multiple content types, but overall every section earns its place and the key information is front-loaded.

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

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

Despite 10 parameters and no output schema, the description covers the core resizing modes and response format. It omits delivery options, but those are thoroughly documented in the input schema. Given the high schema coverage and the presence of sibling tools, this is sufficiently complete for an agent.

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

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

Schema description coverage is 90%, so the baseline is 3. The description does synthesize the relationship between scale and scale_x/scale_y and explains fill mode, but this information largely repeats what is already in the schema's property descriptions. It adds minimal new meaning beyond the schema.

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

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

The description clearly states the operation ('Resize an image') and outlines the two distinct modes (scale by factor, fill exact box). It is specific about the resource and scope, but it does not explicitly differentiate from sibling tools like crop_image or convert_image, so it stops short of a 5.

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

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

The description provides explicit guidance on when to use each mode: 'Use scale for uniform scaling, or scale_x/scale_y for independent axes' and 'Alternatively set width+height for fill mode'. It also notes the modes are mutually exclusive, giving clear context. However, it does not mention when to use this tool versus alternative sibling tools.

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