Normalize H&E stain

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

Adds meaningful context beyond annotations: names the algorithm (Macenko), states the original is unchanged and a new image is created, which is consistent with readOnlyHint=false and destructiveHint=false. It does not address idempotency (annotations say non-idempotent) or failure modes, so it stops short of full transparency.

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?

Front-loaded with the core action and the key fact (saved as a NEW image) in the first line; the method and rationale follow. Slightly wordy with a redundant closing 'Returns the new image,' 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?

Output schema exists, so return values need not be described, and the non-destructive behaviour plus algorithm name are supplied. For a single-parameter mutation tool with annotations covering safety profile, the only missing piece is idempotency/failure behaviour.

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% and the single image_id parameter is fully documented there, including that it comes from list_images or an upload. The description adds no parameter-level detail, so baseline 3 applies.

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?

States a specific verb+resource (normalize an H&E image's colours via Macenko) and immediately clarifies the output is a NEW image, distinguishing it from transformation-in-place siblings like update_image_metadata. An agent can tell exactly what this does without opening 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?

Gives clear context for when it is useful: slides from different labs/scanners differ in pink and purple tones, and normalizing makes them comparable and helps models such as detect_nuclei. This implicitly routes the agent (run before detection/classification), though it does not state explicit exclusions or prerequisites (e.g. requires an H&E slide).

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