calculate_well_survey

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

With no annotations, the description carries full burden. It discloses the calculation method and output but omits behavioral details like input array length constraints, ordering requirements, or error handling. Adequate but not comprehensive.

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 concise with a clear opening sentence and a structured args list. It is front-loaded with the main purpose. Minor verbosity in the args list but still helpful.

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?

Given the output schema exists (so return values are covered), the description sufficiently explains inputs, method, and outputs. It lacks discussion of edge cases or limitations, but overall complete for typical use.

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 0%, so the description adds crucial meaning: it explains that md is a list of measured depths, inclination in degrees from vertical (0-180), azimuth in degrees from north (0-360), and unit with default 'feet'. This goes well beyond the schema titles.

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 clearly states the tool calculates well trajectory using the minimum curvature method, specifying input (MD, inclination, azimuth) and output (North, East, TVD, dogleg severity). It distinguishes itself from siblings like calculate_dogleg_severity by covering full trajectory.

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 explains what the tool does but does not provide guidance on when to use it versus alternatives (e.g., calculate_dogleg_severity for just dogleg). An explicit usage context would improve clarity.

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