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

Annotations only declare readOnlyHint=true; the description adds cost ('costs nothing'), edge-case behavior ('vergleichbar: false' with a reason, nothing guessed), thresholds that trigger a finding (8 mired, L* 4, ΔE 3), the per-call cap, and the exact shape of the returned grading suggestion. That is substantial behavioral disclosure beyond the 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?

Front-loaded with purpose and workflow, then outputs, then caveats. It is dense but most sentences carry actionable content; the implementation aside (ORB + homography) and the measured 5000 K justification are slightly verbose for an agent that only needs to know the method is robust, but they do support trust in the result.

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 and only a readOnly annotation, the description carries the full burden and meets it: it describes inputs, the comparison method, success and failure result shapes, thresholds, limits, and the downstream tool call. Nothing needed to invoke or interpret the tool correctly appears to be 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 goes further: it explains what `referenz_clip_id` means conceptually (the reference shot every other shot is matched against), what `szene` defaults to, and how `clip_ids` bypasses the lab_shots scene derivation. It adds interpretive meaning rather than repeating the schema text.

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 first sentence states a specific verb and resource — checking colour continuity of a scene in the edit — and immediately names the mechanism (reference-shot comparison at matched scene points) and the outputs (`befund`, `vorschlag.grade`). An agent can distinguish this from generic grading tools (lab_grade_clip, lab_grade) because the description frames it as a diagnostic that feeds another tool.

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 a clear workflow: get scenes from lab_shots, pass the resulting `vorschlag.grade` to lab_grade_clip, then re-run lab_look_match; and it names the fallback path (supply `clip_ids` when lab_shots output isn't available). It also states the operational limit of 24 shots per call. It does not, however, differentiate itself from the plausible siblings lab_continuity_check or lab_look_in_post, so no explicit when-not guidance.

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