CovaSyn Chemistry Tools

covadoe_power_analysis

Statistical power ASSUMING AN ORTHOGONAL ±1 DESIGN — prefer covadoe_design_power.

Takes only scalars, so it never sees the design and cannot check its own premise. It uses
`ncp = n_runs · effect_size²`, which requires `(X'X)⁻¹_jj = 1/n_runs` — true for two-level
factorial and Plackett-Burman designs, and FALSE for a central composite, a Box-Behnken, a
definitive screening design, or a plain factorial WITH CENTRE POINTS.

On those it is wrong by a lot, and confidently: 0.8677 against a true 0.6228 on a Box-Behnken,
0.3874 against 0.2909 on a DSD, 0.3874 against 0.2491 on a 14-run factorial with centre points
(each Monte Carlo confirmed, 2026-08-07).

Use `covadoe_design_power` for ANY design carrying centre or axial points — it builds the design
and takes the non-centrality from `(X'X)⁻¹`, needs no data, and also reports what this tool would
have claimed. Use this one only when the design really is an orthogonal two-level design.
Every response carries `assumes_orthogonal_design: true` and a `validity` note.

Input Schema

Name	Required	Description	Default
`alpha`	No
`n_runs`	Yes
`api_key`	No
`n_terms`	Yes
`effect_size`	Yes
`residual_variance`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 9, 2026

TDQS

A4.4/5.0

Behavior5/5

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

With no annotations present, the description carries the full burden and does so unusually well: it discloses the internal formula (ncp = n_runs · effect_size²), the assumption that makes it valid ((X'X)⁻¹_jj = 1/n_runs), the fact that it cannot self-check its premise, quantified failure cases with Monte Carlo evidence, and the response fields it always emits (assumes_orthogonal_design, validity note). This is exactly the behavioral disclosure a numeric tool needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the assumption and the sibling pointer, and every sentence serves the warning purpose. The three verbatim Monte Carlo comparisons with dates are arguably more evidence than an agent needs to act, which costs a point, but the density earns its space for a caveat-heavy tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be explained, and the description still flags the always-present assumes_orthogonal_design and validity fields. The main remaining gap is parameter-level meaning, which is left entirely to an undocumented schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across 6 parameters, so the description must compensate. It implicitly clarifies n_runs and effect_size through the ncp formula, but n_terms, alpha, residual_variance and api_key are never defined or given units, defaults, or expected ranges. Partial compensation only.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence names the exact operation (statistical power) and the precise modeling assumption it is built on (orthogonal ±1 design), and immediately contrasts itself with the sibling covadoe_design_power. An agent can distinguish it from every other covadoe_* power tool without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It states explicitly when to use it ('only when the design really is an orthogonal two-level design') and when not to (any design carrying centre or axial points), naming the alternative and the reason to prefer it. Conditions are concrete: CCC, Box-Behnken, DSD, factorial with centre points.

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

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