Cleanor

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

Annotations already mark readOnlyHint=true. The description adds behavioral context: the tool reports benchmark results from a controlled study and mentions the HEIC conversion tax, going beyond the annotation. 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?

The description is two sentences, front-loaded with the core purpose, and every sentence adds value. No unnecessary words.

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 is read-only with good schema and annotations, the description covers purpose, usage context, and key output (savings). The absence of an output schema is mitigated by the description's hints about what is returned. Could be slightly more explicit about return format.

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% with clear descriptions for both parameters (format enum and quality enum with SSIM values). The description does not add additional parameter meaning beyond the schema, 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 specifies the verb 'report' and resource 'storage savings of next-gen image formats compared to JPEG', including a specific source (Cleanor Labs benchmark) and an additional metric (HEIC conversion tax). It distinguishes from siblings like optimize_image and storage_capacity.

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 the tool's use case: 'to justify a format choice when building a site or app.' It provides clear context about when to use it, though it does not explicitly mention when not to use or list alternatives.

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=true and openWorldHint=true, covering safety and external access. The description adds that the tool fetches from a public URL and returns sizes, but does not disclose any additional behavioral traits (e.g., no mention of rate limiting, caching, or file size limits). With annotations present, the description provides adequate but minimal extra value.

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 no unnecessary words. The first sentence clearly states the action and output, the second provides usage context. Information is front-loaded and 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?

With no output schema, the description adequately states the return value (optimized image and byte sizes). All parameters are documented via the schema. The tool is not complex. Minor omission: does not specify that the image URL must be publicly accessible, though it is implied by 'public URL'. Overall sufficient for an AI agent to use 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?

Schema description coverage is 100%, so baseline is 3. The description adds context (e.g., 're-encode it smaller' implies format parameter) but does not significantly expand on the already clear schema descriptions. Example: the width parameter is described as 'optionally resizing to a target width' which is already in the schema. No new semantic details.

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?

Clearly states the verb 'fetch' and 're-encode', the resource 'image from a public URL', and the output 'optimized image plus before/after byte sizes'. Distinguishes from siblings by focusing on image optimization for web delivery, unlike image_format_savings (which suggests reporting) or qr_code/storage_capacity.

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?

Explicitly states when to use: 'when an AI-generated or dropped-in asset... is too large to ship.' This provides clear context. No explicit when-not or alternatives, but the sibling names imply alternative use cases (e.g., image_format_savings for analysis, not conversion).

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=true, so the description doesn't need to reiterate non-destructive behavior. It adds output format details ('SVG') and quality ('dependency-free'), but no additional behavioral traits like rate limits or auth.

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?

A single, well-crafted sentence delivers all necessary information without any wasted words.

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 generation tool with no output schema, the description adequately explains the return format and usage context. It lacks error-case details but is sufficient for the tool's simplicity.

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 schema already explains all parameters. The description adds no extra meaning beyond the parameter 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 clearly states it encodes text or URLs into QR code SVGs, with a specific verb ('Encode... as a QR code') and resource ('return... SVG'). It distinguishes from sibling tools like optimize_image and storage_capacity.

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 when a QR code SVG is needed, but does not explicitly state when not to use or compare to alternatives. However, siblings are unrelated, so context is sufficient.

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 indicate readOnlyHint=true and openWorldHint=false. The description adds value by disclosing that results are 'corrected for real OS/filesystem overhead' and 'backed by Cleanor Labs measured per-item sizes', which is behavioral context beyond 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?

The description consists of two well-structured sentences. The first sentence immediately states the purpose and key correction. The second sentence cites the data source and provides use cases. No waste.

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 two fully described parameters and no output schema, the description is complete. It explains the core function (counting items with overhead), data source, and typical use cases. No 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% with parameter descriptions. The description adds context about correction and data source but does not significantly enhance parameter meanings beyond what the schema provides. 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 computes how many photos or minutes of video fit in a given storage size, with real OS overhead. It distinguishes from siblings like image_format_savings optimize_image and qr_code which are unrelated.

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 use cases: 'Use for realistic sample copy dashboards or how many photos fit in 128 GB answers.' It does not mention when not to use or alternatives but the use cases are clear and contextually appropriate.

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