load_map

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

With no annotations provided, the description carries the full burden and excels: it discloses failure behavior ('header is parsed before PyMOL is touched... malformed file fails without leaving a half-loaded object'), the 'load is confirmed rather than assumed' behavior, and the provenance default of 'unknown' with no inference. This is rich, specific behavioral context.

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 front-loaded with the core purpose in the first sentence, but the second paragraph on provenance is somewhat verbose and philosophical. Each sentence adds value, though a more compact explanation could achieve the same effect.

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?

The tool has a modest 3-parameter schema and an output schema, so little return-value documentation is needed. The description covers error handling, provenance semantics, and reporting behavior, making it fully adequate for correct invocation and interpretation.

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?

The input schema covers all parameters with 100% coverage, including descriptions for path, name, and provenance. The description adds rationale for the provenance default but no new parameter-level semantics, so the baseline of 3 is appropriate.

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 opens with 'Load a volume into PyMOL, recording where it came from and reporting its geometry,' which names a specific verb, resource, and distinct purpose. It clearly differentiates from sibling tools like load_structure by focusing on volume maps rather than atomic structures.

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 clearly establishes the tool is for loading volumes into PyMOL, and the provenance discussion explains how callers should think about declaring provenance. However, it does not explicitly name alternative tools or state when not to use this tool, so it stops short of full exclusion guidance.

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