jp_lit_suggest_classification_codes

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

Beyond the annotations, the description discloses the local write side effects (session history update even on cache hit, cache creation/replacement on miss or force_refresh), the non-destructive nature, and the fact that external sources are not modified. This meaningfully complements the annotations and sets correct expectations for a non-idempotent, open-world 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?

The description opens with a compact safety/effect classification, then states the operation, routes to siblings, and closes with cache behavior. Every sentence earns its place; there is no repetition of schema fields or filler.

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 6-parameter tool with a rich output schema, the description supplies the missing decision context: external data sources involved, the exact filter compatibility of the returned parameter, the sibling disambiguation, and the local-storage behavior. No critical information needed to invoke it correctly is absent.

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 schema already documents all six parameters. The description adds only high-level context (that the output feeds CiNii Books category filters and includes a jp_lit_search call), not per-parameter semantics, so the baseline of 3 is appropriate.

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 the exact operation (suggest NDC/NDLC classification codes from a subject term via Web NDL Authorities), the return artifact (suggested_category_param + jp_lit_search call example), and explicitly distinguishes the reverse-direction sibling jp_lit_find_authority_terms_by_classification and the actual-search sibling jp_lit_search. It leaves no ambiguity about what this tool uniquely does.

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?

It states the expected use case (subject term -> classification codes) and gives explicit alternatives: use jp_lit_find_authority_terms_by_classification for classification-to-term lookup and jp_lit_search for actual literature search. This is exactly the when/alternative guidance an agent needs to choose the right tool.

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