Mne Apply Inverse

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

No annotations are provided, so the description carries the behavioral burden. It does so reasonably well by stating that the tool 'Stores the source estimate (stc)' and 'reports the peak activation time,' which discloses the main side effect and output beyond the schema. It does not mention overwriting an existing stc with the same name or computational cost, but the central behavioral contract is explicit.

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?

Three sentences, each carrying distinct information: the operation and inputs, method options, storage/report behavior, and pipeline partners. There is no filler or repetition of schema defaults.

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 description establishes the pipeline role, method choices, and default result variable, but for a 6-parameter, annotation-free tool it leaves gaps: what snr controls, whether an existing stc is overwritten, and how the result feeds mne_plot_source_estimate are not stated. An agent can invoke it with defaults, but not with full contextual confidence.

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?

Only method and stc receive meaningful explanation: method gets its value list, and stc is identified as the stored source estimate. The remaining parameters—snr, cov_name, fwd_name, and evoked_name—are left to name-based inference. With 0% schema description coverage, this is insufficient compensation for the input schema's lack of descriptions.

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 opening clause 'Estimate cortical sources from an Evoked using a forward model and noise covariance' names a specific verb, object, and data dependencies, clearly distinguishing it from siblings like mne_plot_source_estimate or mne_apply_ica. It also lists the supported methods and the stored result, so the agent knows exactly what the tool acts on.

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 final sentence gives an explicit pipeline cue: 'Pair with mne_make_forward + mne_compute_noise_cov,' telling the agent which prerequisites must exist before invocation. It does not list exclusions or alternative tools for the same job, so it stops short of full when/when-not guidance.

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