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

With no annotations, the description carries the full burden and does well: it explains the decision logic (Mandel's F-test vs. R-squared threshold), the exact conjunction required for `overall_passes`, the "not_computed" degraded path when `noise_rms` is absent, and hard bounds. It stops short of stating that the call is a pure computation with no persistence, or how `api_key`/authentication is handled.

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 statistics are front-loaded in one line and the parsing constraints follow immediately; every sentence adds information. The closing bounds sentence ("`max_residuals` (500) on the residuals echoed back") is telegraphic and slightly harder to parse than the rest.

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 7-parameter statistical tool with no annotations, the description covers inputs, pass criteria, degraded paths, and limits, which is what an agent needs to call it correctly; an output schema exists so return structure needn't be spelled out, though the description usefully names `overall_passes`, `criteria`, and `lod_loq.status`. The only real omission is auth/`api_key` context and any sibling routing.

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%, so the description must compensate, and it largely does: it gives format (comma-separated, equal length), a minimum cardinality with an example for `concentrations`/`responses`, the role of `noise_rms`, and the meaning of `precision_measurements` as repeats. `api_key` and the effect of the `max_points` default per series are not explained.

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 line names a specific regulatory standard and the exact statistics computed (linearity, precision, LOD/LOQ), so the verb+resource is unambiguous. It does not, however, distinguish itself from nearby siblings such as covnmr_qnmr_advanced or covauv_validate, leaving the agent to infer the boundary.

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?

Usage is implied through input requirements ("AT LEAST FIVE points", "Give `noise_rms` for LOD/LOQ") and the criteria for `overall_passes`, which tells the agent what data it must supply. There is no explicit statement of when to prefer this over a sibling validation or QNMR tool, and no exclusions.

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