image-resize-convert-compress-watermark

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

Annotations provide no positive hints (all false), so the description carries the behavioral burden. It discloses output naming, aspect-ratio preservation, width/height relationship, and the 5-file/4MP limit. It does not restate safety properties, but nothing contradicts the annotations.

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?

Three sentences, no filler, with the core command front-loaded and supporting constraints packed efficiently. Every sentence earns its place.

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 main behavior, naming, sizing rules, and quotas, while the schema covers path/out_dir semantics. A minor gap is that width/height are optional in the schema and the description never states what happens when neither is provided.

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?

With 100% schema coverage, the description need not repeat parameter names, but it adds genuinely new semantics for the width/height interaction—'one lets the other follow' and 'both fit inside the box'—which is not inferable from the schema alone.

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 a specific verb and resource—'resize every image in paths at once'—and adds the output naming pattern, making the batch behavior unmistakable. This distinguishes the tool from sibling image_resize without needing the schema.

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 clearly states when to call the tool: when all images in paths should be resized in one operation. It does not explicitly name alternatives like image_resize for single-image jobs or give when-not-to-use conditions, but the batching context is unambiguous.

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?

Annotations only say the operation is not read-only and not destructive; the description adds that the response reports byte counts, that quality affects only JPEG, that PNG shrinks only via max_width, and that the answer names which method worked. It also states a free-tier source-size limit. Without an output schema, this return-signaling information is useful.

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?

Two sentences carry purpose, format-specific behavior, and a usage limit with no filler. The main goal is front-loaded and every clause earns its place.

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 six parameters and no output schema, the description plus the 100%-covered schema give an agent enough to invoke the tool correctly and know what to expect back. It could be slightly stronger by naming sibling alternatives, but the free-tier limit and return behavior fill the important gaps.

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 100%, but the description goes further by linking parameters to formats: `quality` is JPEG-only, `max_width` is the PNG compression path, and the response identifies which parameter was used. This cross-parameter behavior is not present in the JSON 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 specifies a concrete action ('re-encode an image smaller') and a measurable outcome ('report byte counts before/after'), which clearly separates this from sibling resize/convert tools. The PNG/JPEG behavior also ties the purpose to file-size reduction rather than dimension changes.

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 opens with 'Call this tool to re-encode an image smaller,' telling an agent when to reach for it. It does not name alternative tools or state when not to use it, but the format-specific caveats (JPEG vs PNG) provide clear operational 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?

The description discloses behaviors beyond the annotations: quality only applies to JPEG, transparency flattens to white on JPEG output, and free-tier sources are limited to 4 MP. Annotations are all false, so the description carries the burden of explaining side effects and limits; it does so without contradicting any annotation.

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 reasonably short and front-loaded, but the phrase 'the answer says so' is confusing filler and the quality note partially repeats the schema. It earns its place overall, but the odd wording reduces clarity and polish.

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 conversion tool with five parameters and no output schema, the description, combined with the rich input schema, covers the essential context: formats, quality semantics, transparency behavior, and source-size limits. It does not explain the output download behavior, but the schema already covers out_path, so nothing critical is missing.

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%, so the parameters are already well documented. The description adds a small behavioral note about quality being JPEG-only and transparency flattening, but most of that is already present in the schema property descriptions. Therefore it meets the baseline without adding substantial parameter-level clarity.

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 states a specific action ('re-encode an image') and explicitly enumerates the target formats, which unambiguously distinguishes this tool from sibling resize, compress, crop, and watermark tools. The verb is active and the resource is clear.

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 clearly frames when to call the tool ('Call this tool to re-encode an image as...') and gives practical constraints such as JPEG-only quality and free-tier source size. It does not explicitly mention alternatives or when not to use it, but the format-conversion purpose is clear enough to route an agent.

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 annotations all false, the description adds meaningful behavioral context: out-of-bounds rectangles are refused with the real image size rather than clamped, and there is a 4 MP limit for free sources. These are exactly the non-obvious runtime behaviors an agent needs.

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?

Three short sentences with no filler: purpose, coordinate semantics, edge-case behavior, and a usage limit. Every sentence contributes information an agent needs before calling.

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 7-parameter mutation-like tool with no output schema, the description plus fully documented schema covers the required inputs, coordinate system, error behavior, and a key size limit. It does not explicitly describe the shape of the result, but out_path's schema description already covers the download link, so nothing critical is missing.

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 100%, so the baseline is 3, but the description adds useful semantics by defining (x, y) together as the top-left corner and explaining how out-of-bounds rectangles are handled. It complements rather than repeats the schema definitions.

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 uses a specific verb and object – 'cut a rectangle out of an image' – which is unambiguous and naturally distinct from sibling tools like image_resize, image_thumbnails, or image_convert. Even without naming a sibling, the action of cropping is precisely identified.

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 explicitly frames the tool as the action to take when a rectangular crop is needed ('Call this tool to cut a rectangle...'). It does not name alternatives or state when not to use it, so it stops short of a full 5, but the usage trigger is clear.

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 behavioral disclosure burden. It states the deletion scope (one image per token, uploaded or generated) and the refusal behavior for unknown names, including that nothing is deleted. It does not explicitly say deletion is permanent or irreversible, but 'delete' strongly implies it and the token scoping is useful context.

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?

Three short sentences with no filler. The core action is front-loaded, followed by the name semantics, a list source, and the invalid-name edge case. Every sentence earns its place.

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 one-parameter destructive tool with no output schema, the description covers the action, scope, how to discover valid inputs, and error behavior. Nothing essential is missing for an agent to call it correctly.

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 schema has 0% description coverage, so the description must compensate. It does this well by explaining that name is the stored name, and by directing the user to image_files to find valid names. This adds meaning far beyond the raw string type and length constraints 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?

The description uses a specific verb and resource: 'Delete one image stored for your token, uploaded or generated'. It clearly distinguishes the tool from siblings like image_upload and image_files by stating its destructive action and the exact object it operates on.

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 gives clear context for when to use the tool and points to image_files as the way to discover valid names. It does not explicitly name alternatives, but no other sibling performs deletion, so no exclusion is necessary. The invalid-name refusal behavior also sets expectations for misuse.

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?

The description states 'Read-only,' but the annotations set readOnlyHint to false, which directly contradicts the description. The additional details about sampling from a 200 px copy and fixed cost are useful, but under the contradiction rule this dimension scores 1. Annotation Contradiction.

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 compact, front-loaded with the primary purpose, and every sentence adds useful information: output format, sampling/cost behavior, read-only nature, and plan limits. No filler or redundancy.

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 analysis tool with 100% schema coverage and no output schema, the description covers the key things an agent needs: what it returns, how it samples, cost behavior, and count limits. The only completeness gap is the conflicting read-only claim, already penalized under behavioral transparency.

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 100%, so baseline is 3. The description adds meaningful parameter behavior beyond the schema: 'Free gives the top 3; Pro up to 16' clarifies how the count parameter behaves by plan, and the path's source is already clear from the schema. This extra plan-dependent nuance raises it to 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 opens with a clear, specific action: 'read the colours covering most of an image' and specifies the output format as hex codes with pixel share. This clearly distinguishes it from sibling tools like image_info, image_resize, or image_compress.

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 explicitly says when to call the tool ('Call this tool to read...') and gives useful context about cost and plan limits. It does not name an alternative tool or state when not to use it, but among the image manipulation siblings the intended use is clear.

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?

There are no annotations, so the description carries the full behavioral burden. It discloses the scope (images for your token), the two categories included, and that sizes are returned. However, it does not mention whether the operation is read-only, what the output format is, or whether pagination or ordering applies. For a simple listing tool this is adequate but not fully transparent.

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 a single compact sentence that front-loads the core action, then efficiently adds the key scoping and content details. Every element adds value, with no redundant wording.

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 the tool's low complexity, zero parameters, and lack of an output schema, the description covers the essential purpose, scope, and included content. It is slightly incomplete because it does not hint at the output structure (e.g., file names, paths, or format), but for a simple listing operation the gap is minor.

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 input schema is empty, so there are no parameters requiring explanation. The description correctly adds no parameter-related noise. With zero parameters, a baseline of 4 is appropriate because the parameter semantics burden is nonexistent.

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 uses the specific verb 'List' and clearly identifies the resource: images stored for the caller's token. It further distinguishes the tool by covering both uploaded images and server-generated images, which sets it apart from sibling mutation tools like image_upload, image_resize, and image_delete_upload.

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 communicates a clear retrieval use case: when you need to see all images associated with your token. It does not explicitly name alternatives or exclusions, but among the sibling tools it is the only listing operation, so the intended usage is fairly evident.

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?

The description explicitly discloses read-only behavior ('Read-only, never modifies the file') and rate/size limits ('Free tier: unlimited, any size'), which goes beyond the all-false annotations. Since the annotations do not make a positive claim, there is no contradiction.

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?

Two focused sentences: the first front-loads the purpose and return fields, the second covers constraints. Every sentence earns its place with no filler.

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 single-parameter inspection tool with no output schema, the description fully covers what the agent needs: the input, all returned data fields, read-only safety, and usage limits. Error cases are not described, but the low complexity makes this sufficient.

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 input schema already fully documents the single parameter ('path') with high coverage, so the description adds no additional parameter-level meaning. Baseline 3 applies because the schema carries the explanatory burden.

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 states a specific verb ('inspect'), a specific resource ('an image'), and enumerates the exact information returned (format, pixel dimensions, megapixels, file size, alpha channel). This distinguishes it clearly from sibling tools like image_dominant_colors or image_files.

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?

'Call this tool to inspect an image' provides an explicit when-to-use context, and the free-tier note adds practical guidance for using it as a lightweight inspection step. It does not explicitly name alternatives or when-not conditions, but the context is clear.

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?

The description adds 'Input untouched,' clarifying non-destructive behavior beyond the annotations, and notes the 4 MP source limitation. It does not spell out overwrite behavior or return details in the main description, but the schema covers overwrite and the output download link.

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?

Four short sentences, each carrying distinct information: purpose, fit semantics, aspect-ratio behavior, input safety, and size limit. No filler, repetition, or irrelevant detail.

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 6-parameter tool with full schema coverage and no output schema, the description covers the essential behavioral decisions: fit modes, one-dimension handling, input safety, and size cap. It does not say what happens if both width and height are omitted, and it doesn't route to sibling tools, but the remaining gaps are minor.

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 100%, so the baseline is 3, but the description adds real value by explaining what each fit mode does ('cover fills and crops', 'exact stretches') and clarifying that a single width or height alone preserves the aspect ratio. This goes beyond the schema's terse field descriptions.

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 a clear verb+resource: 'write a resized copy' and explains the three fit modes. It does not explicitly contrast siblings like image_batch_resize or image_crop, but the resize semantics are unambiguous and distinct from those tools.

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?

'Call this tool to write a resized copy' directly states when to invoke it, and the free-tier size cap gives practical selection context. It does not mention when to prefer image_thumbnails, image_batch_resize, or other image tools, so it lacks explicit alternative routing.

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?

The description discloses important behavioral details beyond the annotations: it decodes and re-encodes from raw pixels, drops specific metadata types, and has a 4 MP free-tier limit. Annotations indicate mutation is possible, and the description describes the write-copy behavior consistently without contradiction.

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?

Two tightly written sentences convey the core behavior, the metadata dropped, and the free-tier constraint. Every sentence adds value, and the most important behavior 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?

The description plus a fully documented input schema provide enough context for correct invocation. The out_path schema covers the download-link behavior, and the description explains the transformation. Minor gaps remain, such as behavior above the free tier, but these are not essential for basic use.

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 input schema already provides 100% coverage for all three parameters with clear descriptions. The tool description does not add parameter-specific semantics beyond the general purpose, so the baseline score 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 states a specific action: write a copy containing only pixels, dropping all metadata. It clearly differentiates this from sibling operations like image_compress, image_convert, or image_watermark by focusing exclusively on metadata removal.

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 gives clear context for when to call this tool: when metadata such as EXIF, GPS, camera, and colour-profile data must be removed. It does not explicitly name alternatives or state when not to use it, but the intended use case is unambiguous.

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?

It adds useful traits – atomic path reservation ('collision leaves nothing half-done') and a rate limit ('Free: 5/call, 4 MP'). However, it states outputs are written into out_dir, whereas the schema asserts out_dir is ignored and outputs are download links, so the description actively misrepresents a central behavior. With all annotations false, this inaccuracy is not offset by structured signals.

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?

Three sentences deliver the core action, an atomicity guarantee, and a cost limit with no filler. The key behavior is front-loaded in the first sentence.

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?

This tool has no output schema and no meaningful annotations, so the description must stand alone. It omits that results come back as temporary download links (left to the schema) and incorrectly presents out_dir as a real target. The atomicity and cost notes help, but the misleading output channel is a critical gap.

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 100%, so the baseline is 3. The description adds meaning for size by defining the fit and aspect behavior, and for the output path by giving the '<name>-thumb.<ext>' pattern. However, its characterization of out_dir clashes with the schema's explicit 'ignored' behavior, so the added value is partly undermined.

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 'Call this tool to write one thumbnail per input into out_dir' – a concrete action with a specific output naming pattern. It states the resizing constraint ('fit inside size by size, aspect kept') and makes clear it produces thumbnails, though it never explicitly differentiates from sibling tools like image_resize or image_batch_resize.

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?

No guidance contrasts this tool with the many image siblings; it simply instructs to call it for thumbnails, leaving an agent to infer when that is preferred over a plain resize. There are no exclusions, prerequisites, or alternative references.

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 and does so thoroughly. It discloses file format handling via magic bytes, the 256 KB request cap, URL restrictions (timeout, redirects, public hosts, 2 MB cap), persistence of uploads per token, cleanup tools, and download link expiry. This is far richer than a generic 'upload an image' statement.

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 long but information-dense; every sentence contributes operational detail. It is top-loaded with the core purpose and then expands into necessary constraints. It could be slightly better structured with bullet points, but no sentence is wasted.

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 is very complete for a tool with no annotations and no output schema: it covers input formats, size limits, URL behavior, storage lifecycle, and related tools. Minor gaps remain around the exact upload response shape and behavior on duplicate names, but the agent likely has enough to call the tool correctly.

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?

Although the schema already describes all three parameters, the tool description adds crucial semantics not encoded in the schema: 'Give exactly one of image_base64 or url,' the recommendation to prefer url above 10 KB, detailed url constraints, and the meaning of `name` as the identifier later passed to other tools. This materially improves an agent's ability to invoke parameters correctly.

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 a specific action, 'Send an image to this hosted endpoint,' and clearly identifies the tool's role in the workflow: upload once, then pass the name as `path` to downstream image tools. It distinguishes itself from siblings by explaining that this is the upload step rather than a processing/manipulation step.

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 explicitly explains when to use image_upload versus other tools: 'There is no filesystem here, so instead of a path you upload the file once... and then pass its name as path.' It also gives concrete decision guidance between base64 and url inputs, including size thresholds and constraints, so an agent can select the correct invocation mode.

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?

Annotations are all false and convey little, so the description productively adds the critical runtime trait: the tool cannot execute on this endpoint and why. It doesn't describe side effects, but since invocation is blocked, the most relevant behavior is disclosed.

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?

Two sentences with zero filler; the critical unavailability message is front-loaded and the fallback path is stated compactly. The URL is necessary for the alternative and not excessive.

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 that is unavailable on this endpoint, the description fully equips the agent: it says not to invoke it, explains why, and supplies the workaround. The rich parameter schema covers the remaining invocation details for a local install.

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?

All six parameters have descriptions in the schema, including defaults, constraints, enums, and effects, so the schema carries the parameter-load. The description adds no parameter-level information, matching the baseline-3 case.

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 title and the repeated word 'watermark' make the operation clear, but the description itself never states 'adds a text watermark to an image' as an active definition. It instead leads with unavailability, so the purpose is inferable rather than explicitly specified.

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 instructs the agent not to use this tool on the hosted endpoint, gives the technical reason (missing bitmap font files), and names concrete alternatives: a local install with a download URL or uploading an already-watermarked image. This is exemplary when-not-to-use guidance.

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?

Annotations already indicate this is not read-only and not destructive, and the description adds useful behavioral detail: existing data under the token stays, and a wrong or expired key changes nothing. This goes beyond the annotation hints by explaining failure semantics and safety of the 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 description is two sentences with no filler. It front-loads the core action, then gives the key format and safety guarantees, and closes with the verification tool. Every sentence contributes essential information.

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 single-parameter activation tool with annotations covering the safety profile, the description is complete: it states what the tool does, what input is needed, what happens on failure, and how to confirm success. There is no missing critical information for an agent to call it correctly.

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 schema already documents the key parameter and its format with 100% coverage. The description adds meaning by specifying that the key must be issued at checkout for this server or the bundle, which clarifies validity requirements not present 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?

The description uses a specific verb and resource: it 'turns Pro on' for the connection using a license key. It also distinguishes itself from the sibling license_status tool by saying license_status confirms the activation. This leaves no ambiguity about what the tool accomplishes.

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 clear context for use: the key must be an MCPL1.<payload>.<signature> issued at checkout for this server or the bundle. It does not explicitly list exclusions or alternative tools, but it does reference license_status as the confirmation path, giving the agent a sense of the workflow.

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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds useful context beyond annotations by specifying the JSON response contents and stating 'nothing changes,' reinforcing the read-only nature of the call.

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?

Two dense sentences cover output, purpose, usage context, and side-effect guarantee. Every sentence earns its place and the most important 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?

With no output schema, the description sufficiently describes the return value. With no parameters, annotations covering safety, and a clear use case, nothing essential is missing for an agent to invoke this tool correctly.

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?

There are zero parameters and the schema already fully documents that fact. The description explicitly says 'No arguments,' which is accurate and reinforces the schema without needing further elaboration.

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 begins with a specific verb ('Report') and a precise resource ('this endpoint's licence state for your token'), and enumerates the exact output fields: product, tier, reason, and checkout URL. This clearly distinguishes it from sibling tools like license_activate.

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 states when to call it: 'Call it to explain a free-tier refusal.' This is clear contextual guidance, though it does not name alternatives or explicitly state when not to use it.

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