get_probe

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

With no annotations, the description must carry the burden; it discloses that labels are 'where known' (partial data possible). It does not say whether the call has side effects, how missing contacts are represented, or how max_rows affects results, so transparency is only partial.

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 short sentences, no filler or schema repetition; the content sentence is front-loaded and the usage note earns its place.

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?

For a simple two-parameter getter this is minimally viable: it says what is returned and how to use the output downstream. But with no output schema, no param descriptions, and no note on max_rows, there are clear gaps for an agent that needs to interpret the response.

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%, and the description does not mention session_id or max_rows; the only parameter-ish term is 'channel', which refers to output, not an input. The description adds no meaning to either input parameter.

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 identifies the returned resource as probe contact geometry and brain-area labels per channel, and the 'where known' qualifier sets scope. It does not explicitly contrast with plot_probe or set_probe_geometry, but the data-vs-plot/set distinction is clear enough.

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 second sentence gives an explicit downstream usage: feed returned channel indices into the units argument of get_psth / fit_latent_factors to analyze one area. It does not state when not to use this tool or name alternatives, but the context is clear.

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