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

No annotations are provided, so the description must disclose behavioral traits. It states the tool reports config status without revealing the API key, which is a notable safety disclosure. However, it does not explicitly declare read-only behavior, potential delays, or what happens if the server is unreachable. The disclosure is adequate but not thorough.

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 efficient sentences with zero wasted words. The first sentence front-loads the tool's purpose and scope, the second adds usage guidance. It is appropriately sized for the tool's simplicity.

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 zero-parameter diagnostic tool with no output schema, the description covers what it reports, what it excludes, and when to invoke it. It lacks details on output format, but given the context (image generation siblings), the provided information is sufficient for an agent to select and invoke 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 no parameters (0 params), so per the rubric baseline is 4. The description adds no parameter info, but none is needed. Schema coverage is trivially 100%.

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 reports configuration status of the Foundry image server, listing specific elements (environment variables, endpoint, deployment, output directory) and explicitly excludes revealing the API key. It distinguishes from sibling tools (edit_image, generate_image) which are about image manipulation, not config checking.

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 advises 'Call this first when a generation fails with a configuration error,' providing clear when-to-use guidance. It also states what it does not reveal (API key), which implicitly guides against expecting that output. No explicit when-not or alternatives, but sibling differentiation 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?

The description says the result is written to disk as a PNG and the source file is never modified, which are useful behavioral details. However, with no annotations provided, the description carries full burden. It does not disclose if the operation is reversible, whether it requires any specific authentication or permissions, or any side effects (e.g., whether it overwrites existing output files silently). It mentions the output is a PNG even if the source is JPEG, which is valuable. Overall, it provides moderate transparency but lacks some important details.

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 very concise: three sentences covering purpose, output format, and behavioral assurance. No superfluous words. It is front-loaded with the core action and a clear summary. Each sentence adds distinct value.

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 has three parameters (all with schema descriptions), no output schema, no annotations, and no nested objects, the description provides the essential context for an editing operation with output to disk. It explains the return value (path to the result) despite there being no output schema. However, it does not specify if the prompt can be a plain text instruction or requires specific formatting, nor does it list supported edit capabilities (e.g., only simple changes? complex edits?). For a tool with no annotations, this leaves some gaps in 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% (all three parameters have descriptions in the schema), so the baseline is 3. The tool description adds value beyond the schema by confirming that 'image' must be a path to a PNG or JPEG, 'prompt' is a text instruction for editing, and 'output_path' must end with '.png'. The description contextually reinforces the parameter meanings, helping the agent understand their roles beyond the schema's property 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 that this tool edits existing PNG or JPEG images using gpt-image-2 on Azure AI Foundry, following a text instruction. It also specifies that the result is saved to disk as a PNG and the source file is never modified. This provides a specific verb (edit), resource (PNG/JPEG image), and distinct operation (following a text prompt). It distinguishes edit from the sibling generate_image (which likely creates new images instead of editing existing ones).

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 mentions the supported image formats (PNG, JPEG) and that the source file remains unchanged, but it does not provide explicit guidance on when to use this tool versus the sibling tools check_config or generate_image. There is no 'when to use' or 'when not to use' advice, and no mention of prerequisites (e.g., image must exist, must be a supported format). The agent is left to infer usage context from the tool name and input schema.

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 clearly discloses that the image is written to disk and the path is returned (not inline), and it specifies dimension constraints. However, it does not disclose error behavior, permission requirements, rate limits, or what happens if constraints are violated, leaving room for improvement.

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 output format, followed by prompt guidance and constraints. Every sentence is necessary and informative, with no redundancy or wasted words. It is highly efficient.

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 has 4 parameters, no output schema, and no annotations, the description covers the essential aspects: purpose, output format, constraints, and prompt advice. It is mostly complete but lacks details on error handling, performance, or any prerequisites, which would be nice to have for a generation tool.

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 description adds value beyond the schema by explaining the dimension constraints (min 768px sides, max 1048576 total pixels) and examples of valid sizes. It also adds context about prompt quality. The fact that the tool returns the path is not in the schema but is in the description. This meaningfully supplements the parameter 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 clearly states the tool generates a PNG image using gpt-image-2 on Azure AI Foundry, and distinguishes it from the sibling tools 'edit_image' (editing) and 'check_config' (configuration checking). The verb 'generate' and resource 'PNG image' are specific, and the output behavior (written to disk, returns path) is explicitly noted.

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 actionable guidance: 'Use a detailed prompt covering subject, style, composition, lighting and colors.' It also gives explicit dimension constraints (minimum side 768px, max total pixels 1048576) with valid examples. However, it does not explicitly state when to use this tool versus alternatives (e.g., edit_image), nor does it mention any prerequisites or context for using the tool.

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