Search authors by name. Returns author records (name, birth date, top work, work count) matching the query. Returns: Matching author records..
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Author name to search for, e.g. "Ursula K. Le Guin". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by listing the returned fields (name, birth date, top work, work count) and confirming the search query semantics. It does not mention pagination or edge cases, but for a simple search this is adequate.
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 short but contains redundancy: the first sentence already states that it returns author records with fields, and the second 'Returns: Matching author records..' adds no new information and has awkward punctuation. It is concise overall but not maximally efficient.
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 simple search tool with one well-defined parameter and strong annotations, the description provides enough context: what the tool does, what fields it returns, and an example parameter. No output schema exists, but the return fields are listed, making it reasonably complete for an agent to use correctly.
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 input schema has 100% coverage, with the parameter q described as 'Author name to search for, e.g. "Ursula K. Le Guin".' The description adds little beyond the schema, essentially restating 'by name.' Baseline 3 is appropriate because the schema already provides strong semantics.
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 states a specific verb and resource: 'Search authors by name.' This clearly distinguishes it from the sibling tool search_books, which presumably searches books. It also specifies the return fields, further clarifying its purpose.
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 implies when to use this tool: whenever searching for authors by name. The sibling tool search_books suggests an alternative, but the description does not explicitly contrast them. Still, the context is clear with no exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Both tools are clearly distinct: one searches for authors, the other for books. Although search_books can return books by author name, the result types are different, so an agent can easily choose the right tool.
Both tools follow the same verb_noun pattern: search_ + plural entity. This is consistent and predictable.
With only two tools, the server feels thin for a book search domain. It is borderline but acceptable for a narrow search-only purpose.
Book and author search are covered, but there are no get-by-ID endpoints. Agents cannot fetch specific records after searching, which is a notable gap in the lifecycle.