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

No annotations are present, so the description carries the full disclosure burden. It does disclose return-shape behavior — 'episode context, frameworks, verbatim quotes with timestamps, and links' — which goes minimally beyond the tool name. However, it omits any caveats about content coverage, result freshness, or search quality (e.g., whether quotes are truncated), leaving a modest behavioral gap for an unannotated tool.

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?

Two efficient sentences with zero filler. The verb and resource are front-loaded, the audience is scoped in the first clause, and the use-case trigger is tucked into the closing clause. Every word earns its place — this is concise, dense, and well-formed.

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 two-parameter, no-output-schema search tool, the description is near-complete: audience, return shape, and intent triggers are all covered. Minor gaps include no explicit differentiation from search_leanscale beyond a passing contrast, and no note about result-set limits or handling of no-match queries. Still, for the tool's complexity, this is fully sufficient.

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% — query is documented as 'Topic, guest name, company, or idea' and limit clearly states 'Default 6' with min/max constraints. The description reinforces query semantics with return-type context but adds no parameter-specific detail beyond the schema. Baseline 3 is appropriate since schema already covers parameter meaning and the description doesn't introduce gaps.

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?

Description leads with a specific verb+resource pair ('Search every episode of The LeanScale Podcast') and scopes the audience ('conversations with CROs, VPs of RevOps, and founders'). It distinguishes itself from siblings by specifying unique return artifacts — 'episode context, frameworks, verbatim quotes with timestamps, and links' — and signals differentiation from search_leanscale via 'rather than a methodology.'

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?

Explicitly states when to use the tool ('when someone wants an operator's opinion, a real story, or a quotable line') and provides an exclusion via 'rather than a methodology,' implying when the tool should NOT be used. This is exemplary when/when-not guidance that names the intended query intent without naming a sibling directly.

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