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

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond those annotations: the data is 'embedded as curated static reference', no API key is required, and the data source is the 2023–2024 Kitces Research survey. This meaningfully informs an agent that this is a deterministic, non-live dataset.

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 description is well-structured and scannable, with a clear first sentence followed by a bulleted category list. Each sentence adds information: scope, categories, default behavior, source, and authentication requirement. There is minor redundancy with the title and the source repeating part of the schema description, but overall it is appropriately sized.

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 read-only, idempotent static-data tool, the description covers the essential invocation details: what data is returned, what categories exist, how to use the default, where the data comes from, and that no API key is needed. The output schema exists and the annotations cover safety-related traits, so return-value and mutation details do not need to be described. The only notable gap is the lack of guidance on whether the various generic filtering/tracking params are applicable.

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 description does add useful meaning for the primary category parameter by stating the default value and that category='all' retrieves all categories at once. However, schema description coverage is 0%, and the description does not address the several other parameters present in the schema, such as wholesaler_ids, exclude_fillers, source_resource_id, and additional_display_fields. An agent is left unsure whether those filter-style parameters apply to this static benchmark dataset.

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 opens with a specific verb and resource: 'Return Kitces Research advisor practice benchmark data for independent and RIA-affiliated financial advisors.' It then enumerates five concrete metric categories, which makes the tool's scope immediately clear. This also cleanly differentiates it from all sibling tools, which target holdings, filings, prices, and other market/firm data rather than advisor practice benchmarks.

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 description gives clear context about what data is available, lists the five categories, and explicitly explains the default behavior of category='all'. It does not explicitly name alternatives or state 'use X instead,' but the specificity of the benchmark domain makes the intended usage obvious relative to the sibling tools. This is clear context without exclusions.

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