pixera_recommend_calibration_method

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

With no annotations, the description carries the full behavioral burden yet only says it 'recommends.' It does not clarify that this is a non-mutating advisory call, whether it depends on existing project state, or what deterministic criteria drive the recommendation (versus the actual calibration performed by pixera_run_calibration).

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 front-loaded sentence that lists the possible outputs without filler. It is tight and readable, though its brevity is partly the cause of the gaps elsewhere rather than pure economy.

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?

An output schema exists, so return-value explanation is not required, but for a 5-parameter recommendation engine with zero input documentation and no annotations the description is far too thin. An agent cannot tell which inputs are decisive or how to phrase them to get a useful recommendation.

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 0% across five parameters (surface_type, has_camera, projector_count, accuracy_need, time_budget) and the description compensates for none of it. Terms like 'scenario' gesture at inputs but give no meaning, valid values, or defaults for the parameters that actually shape the recommendation.

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 ('Recommend a calibration approach') and enumerates the candidate methods (homography / PnP markers / VIOSO / MPCDI), so the agent knows exactly what kind of output to expect. It does not, however, explicitly distinguish itself from close siblings like pixera_recommend_warp_method, so a small inference gap remains.

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 only scoping is the vague phrase 'for a scenario'; there is no statement of when this recommendation tool should be used instead of pixera_recommend_warp_method, pixera_run_calibration, or the validate_* siblings. No prerequisites or exclusions are given.

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