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

With no annotations present the description carries the full behavioral burden, and it does disclose the safety profile (read-only, no authentication required) plus the critical semantic caveat that results must not be conflated with check_compatibility. It stops short of describing output shape or any rate/limit behavior, but for a zero-parameter read-only query tool this is solid coverage.

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?

Front-loads the layer tag and the core concept, then what it returns, then usage questions, then behavioral caveats — a sensible ordering. It is information-dense with domain jargon (MDV, axis, evidence direction) but no sentence is 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?

No output schema exists, so the description must at least orient the agent on what comes back, and it does by naming the three kinds of results (zero-contribution rates, zero-marginal directions, bottleneck axis). Combined with the usage framing and the sibling exclusion, it is essentially complete for a no-arg analytic query.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. No parameter-related text is needed or missing.

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 names a specific verb+resource (query Marginal Declared Value / MDV) and enumerates exactly what it surfaces: per-axis zero-contribution rates, evidence directions with permanently zero marginal benefit, and the bottleneck axis. It also explicitly distinguishes itself from the sibling check_compatibility, so an agent can route 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.

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

It gives concrete when-to-use questions ("which axis is cheapest to fill", "does an axis still have evidence value") and an explicit exclusion: "the semantics differ from check_compatibility; they cannot be mixed." That is both positive selection guidance and a named alternative with a boundary condition.

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