mne_decode

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

With no annotations, the description carries the behavioral disclosure burden. It states what the tool does (trains classifiers, cross-validation), what it returns (mean/peak score and plot), and its dependency (scikit-learn). It does not mention side effects, but for an analysis tool that returns results, this is adequately transparent.

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 two sentences, front-loaded with the core purpose, then briefly covers key parameters, return values, and dependency. Every sentence adds value with no filler.

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 6 parameters and 0% schema description coverage, the description gives only partial guidance. It explains the core behavior and return plot, but not how to set up the epochs object, what 'epochs_name' refers to, or how scoring/cv affect results. The output schema exists, but the description still leaves notable usage gaps.

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. It explains cond_a/cond_b as event_id names, but leaves cv, scoring, name, and epochs_name unexplained. Many parameters have defaults, but their roles (e.g., scoring metric, source epochs object) are not clarified, leaving the agent guessing.

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 performs time-resolved decoding (MVPA) by training classifiers at each time point to discriminate two conditions. It names cond_a/cond_b specifically and mentions cross-validation, which makes the tool's purpose distinct from sibling tools like plotting or preprocessing.

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 gives clear context: use this tool for discriminating two conditions via time-resolved decoding. It says cond_a/cond_b are event_id names and requires scikit-learn, which are practical prerequisites. It does not explicitly mention when not to use it or name alternatives, but the context is sufficient for a single-purpose tool.

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