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

Annotations already declare readOnlyHint=true, so safety is covered. The description adds real behavioral context beyond that: the response is a single project's full stage state and includes the `datenmodus` field with its two possible values ('eu' or 'frei'), which the annotations and schema do not convey.

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 tight sentences: the first front-loads the invocation triggers, the second states what is returned and the ID source. No restatement of the name or title, and 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?

With one required param, full schema coverage, a readOnly annotation, and no output schema, the description covers what an agent needs: when to call it, where the ID comes from, and the shape of the returned state including `datenmodus`. Only the disambiguation from other `lab_get_*` reads is absent.

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 parameter is documented as 'The FilmLab project id', so the baseline is 3. The description goes beyond that by telling the agent where the value comes from (`lab_list_projects`), which prevents guessed or fabricated IDs.

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 and resource ('Get a single FilmLab project') plus the payload scope: full stage state including `datenmodus`. It also positions itself against `lab_list_projects` by naming it as the ID source, so an agent can separate fetch-one from list-many without opening a 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?

The opening clause gives concrete triggers — asking where a project stands, what is finished, what comes next, or needing state before continuing. It also routes ID lookup to `lab_list_projects`. It does not disambiguate against other read siblings like `lab_get_state` or `lab_get_context`, which is the only missing piece.

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