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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety class is known. The description adds a small amount of scope ('over the advisors dataset') but says nothing about what the output looks like, rate limits, or whether the count reflects the applied filters.

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?

It is a single short sentence, which is concise, but it is so terse that it front-loads almost nothing useful for invocation. There is no structure to speak of because there is effectively one clause.

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 tool has 11 parameters, a required facet, and an output schema, and the description is adequate only because the schema and output schema carry the rest of the burden. It never explains the relationship between the facet values and the other filters, which is the main thing an agent must know before calling 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?

Schema description coverage is 100%, so all 11 parameters are already documented in the schema and the description adds nothing about the q, facet, or filter semantics. Baseline 3 is the correct ceiling here.

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?

States a specific operation ('Facet aggregation') and dataset ('PitchBook advisors'), which is a clear verb+resource. But it does not differentiate from the sibling tools datasets_pitchbook_advisors_search and datasets_pitchbook_advisors_item, so an agent cannot tell from the description alone whether this returns counts or records.

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?

No when-to-use guidance is given. The description never says to pick this instead of datasets_pitchbook_advisors_search when you want counts or distributions, nor does it mention any prerequisite such as needing the facet parameter valued.

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