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

With no annotations, the description carries the full disclosure burden for what is essentially a static document fetch. It discloses the scope of content well (measurements, verdict logic, limitations, license), giving the agent a solid sense of what reading it yields, though it doesn't state the return format or any access constraints.

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?

A single sentence front-loads the resource and then dense-lists the covered topics, ending with the actionable directive. No sentence is wasted.

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 zero-parameter documentation tool with no output schema, the description explains well what content the document contains and why it matters. It stops short of describing the return format, but the enumerated content scope is sufficient for an agent to decide whether to call it.

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 no parameter semantics to document and the baseline of 4 applies. No misleading parameter hints are present.

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 resource (the methodology of the Digital Experts base) and enumerates its content precisely: hypothesis origins, the $50–500 exploratory test, measurements, verdict rules, limitations and license. This clearly distinguishes it from the data-returning siblings (get_hypothesis, list_niches, niche_stats, search_hypotheses), which fetch records rather than meta-documentation.

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 closing instruction 'Read before interpreting the numbers' gives explicit contextual guidance on when to consult this tool relative to the numeric siblings. It does not name the alternatives explicitly, but the intended sequencing is clear from the wording.

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