Relation vocabulary and canon table

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

With readonly/idempotent/destructive annotations already present, the description still adds valuable non-obvious behavior: relations are stored one-directionally, so a raw relation array always reads outward from the fetched document. This directly affects how an agent would interpret any relation data and is not encoded in the schema or annotations.

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 purpose and then adds the canon table and a critical directionality note. It uses a dense first sentence with a long list and em-dashed asides, making it slightly heavy to parse, but every clause is informative and nothing it wastes words.

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?

Given the tool's care, the description fully covers the available data: predicates, inverses, types, edge counts, canon table structure, and the important usage read-world directionality. The inclusion of a concrete exam seat ('Song of Songs is "Song"') and the instruction to read it before interpreting predicates make it complete even though a schema exists.

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 zero parameters, so there is no parameter meaning to clarify. The baseline for 0 params is 4, and the description does not need to add extra param explanation. It does convey some contextual value by explaining how to use the canon table, but that lies outside parameter 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 clearly identifies the resource as the closed set of graph predicates, listing examples (develops, narrated-in, involves), their inverses, connected types, and edge counts. It also names the canon table with OSIS IDs, slugs, and chapter counts. This makes the tool's unique role as a reference vocabulary unambiguous and distinguishes it from sibling graph-query tools.

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 states 'Read this before interpreting a predicate' and before building a reference, which gives a clear when-to-use trigger. It does not explicitly name alternatives or describe when not to use this tool, but the context makes it evident this is the reference lookup rather than one of the graph traversal or document retrieval tools.

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