load_trajectory_mda

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

With no annotations, the description carries the full burden. It discloses the mechanism (injecting coordinates into states) and the dependency (optional `md` extra), but does not mention side effects such as overwriting existing objects, state count, or performance implications. This is a partial disclosure.

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 sentences, front-loaded with the primary action, and free of redundant information. Every phrase contributes to understanding the tool's purpose and prerequisites.

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 tool has 7 parameters and no annotations, the description covers the essential high-level behavior and a critical dependency. The rich schema fills in parameter details, making the overall package sufficient for an agent to select and invoke the tool correctly.

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 100%, so the baseline is 3. The description adds general context about trajectory formats but does not describe individual parameters; the schema already handles that. It neither enhances nor detracts from parameter semantics.

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 loads MDAnalysis-readable trajectories (e.g., LAMMPS dump, AMBER NetCDF) and explains the mechanism (injecting coordinates into PyMOL states). This distinguishes it from siblings like load_trajectory, which likely handles native formats.

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 provides clear context by naming non-native trajectory formats and mentioning the required `md` extra. It implies use for formats PyMOL cannot natively read, but does not explicitly name alternatives or when-not-to-use scenarios.

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