Find episodes crediting a person

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

The annotations already declare readOnly, idempotent, and non-destructive behavior, and the description adds substantial context beyond that: it reads the Podcasting 2.0 person tag, matches names literally, has a known index-coverage limitation, and returns episodes rather than shows. This gives an agent accurate expectations about results and limitations.

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 front-loaded with the core function and stays compact across four sentences. Each sentence earns its place: behavior, use case, limitations, and result type. There is no wasted wording or redundant restating of the schema.

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 read-only tool with no output schema, the description is complete enough to invoke correctly. It defines the input semantics, explains what results are returned (episodes, not shows), and exposes the two limitations that most affect whether the tool will return useful results.

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 the baseline is 3. The description adds extra semantic meaning for the q parameter by explaining that matching is literal and that inconsistent credits will appear under multiple spellings; max gets no additional description beyond its schema, but the parameter is self-explanatory.

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 and resource: find episodes where a named person is credited in any role. It also distinguishes itself from siblings by explaining it finds real Podcasting 2.0 person-tag credits rather than passing mentions, and by explicitly noting it returns episodes, not shows.

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 clearly frames when to use it: for guest research and tracing where someone has appeared. It also gives honest usage boundaries around person-tag coverage and exact-name matching, but it does not explicitly name alternative sibling tools or state when not to use it in favor of another tool.

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