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

Annotations already cover read-only and closed-world safety, but the description adds material context well beyond them: word_study is an AI-written 2025 essay only partly reviewed, citations in it may not contain the cited word, and the recorded data is limited to definition, forms and occurrence count. That caveat is exactly the kind of trust-calibration an agent needs. It stops short of describing response size or error behavior for an invalid Strong's number.

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?

Front-loaded with purpose and return fields, then caveats and sequencing, which is the right order. The AI-essay disclaimer sentence is long and clause-heavy, but each clause carries a distinct warning, so little is truly wasted.

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?

With no output schema, the description compensates by listing the returned fields and explaining the meaning and ordering of grammatical_forms. Combined with the data-quality caveat and sibling routing, an agent has everything needed to call and interpret this tool 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?

Schema coverage is 100% and the single parameter is fully documented in the schema, including the same H7563/G2316 examples repeated in the description. The description adds only the implicit constraint of a single number; the schema carries the rest, so baseline 3 applies.

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?

States a specific verb and resource ('Fetch the full lexical entry and word study for a single Strong's number') and enumerates what comes back (lemma, transliteration, pronunciation, KJV definition, word_study, grammatical_forms). It is clearly distinguishable from get_strongs_for_verse and get_strongs_occurrences, which it names explicitly.

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?

Gives explicit sequencing ('Call this AFTER get_strongs_for_verse when you need to go deeper on a specific word') and names the two sibling tools to use when verifying whether a verse actually contains the word. It also states the intended use of grammatical_forms versus generalizing from memory.

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