recommend_camera_settings

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description does convey that output is 'conservative starting settings' requiring on-site adjustment, which is useful. However, it doesn't disclose what algorithm/formulas are used, whether any external weather/astro data feeds in, what happens if inputs are inconsistent (e.g., sensor full-frame vs cropFactor 2.0), or what the exact output format/return shape is. Given the complexity of this tool (8 params, 6 required) and zero annotation coverage, significantly more disclosure is warranted.

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 a single, dense sentence with zero filler. Every clause earns its place: 'Generate conservative starting settings' states the action, 'for a camera/lens and subject' names the inputs, and 'photographer must meter and adjust on site' adds the critical caveat. Efficient and front-loaded.

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?

This is a complex tool — 8 parameters, 6 required, no output schema, no annotations, 0% schema coverage. For a tool of this complexity, the description is too thin. It doesn't cover output shape (what settings are returned — ISO, shutter, aperture, or all?), how parameters influence each other, or how results vary by subject type. The single-sentence description is far from complete for an 8-parameter computation tool with no output schema to fall back on.

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%, so the description must compensate — and it does to a degree. The description names the key input categories ('camera/lens and subject'), which maps to the camera, focalLengthMm, maxAperture, and subject parameters. With 0% coverage, even this implicit mapping is valuable, but the description doesn't explain relationship semantics (e.g., how cropFactor interacts with sensor, or that tripod/stabilization affect shutter-speed recommendations). It adds meaning but doesn't fully bridge the 0% coverage gap.

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 uses a specific verb+resource construction: 'Generate conservative starting settings for a camera/lens and subject'. It clearly states what the tool produces (camera settings) and the context (starting/conservative values). It distinguishes itself from the sibling tools which are about astronomy, weather, planning, budgeting, and legal/release tasks — none of which compute camera exposure parameters, so this tool's purpose is uniquely identifiable.

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 implies the usage context ('camera/lens and subject' starting settings) and adds the advisory that 'photographer must meter and adjust on site', which signals these are starting points not definitive answers. It doesn't explicitly state when NOT to use it or name alternative tools, but the sibling set makes the boundaries clear — this is the only camera-settings-specific tool. The on-site-adjustment caveat provides reasonable guidance about expectations.

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