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

Annotations already disclose read-only, idempotent, non-destructive behavior. The description adds meaningful context not captured by annotations: the source is a compilation of multiple primary dictionaries, Thai entries are original scholarly works and therefore always available regardless of ENABLED_LANGUAGES, and the AI client should translate Thai entries if needed. This gives the agent important behavioral knowledge about language availability and fallback handling.

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 slightly longer than necessary but well-structured with a preamble and a dedicated 'About the dictionary sources' section. The emoji and formatting are stylistic but not harmful. Each part contributes: purpose, role, and a critical behavioral note about language availability. It is not bloated, though the source paragraph could be tightened slightly without losing substance.

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 presence of annotations, a 100% schema-described parameter set, and an output schema, the description covers the core information: what the tool does, what sources it uses, and an important caveat about language availability. It does not cover edge cases like multi-word lookups or common failure modes, but those are likely handled by the output schema and the tool's structured response. The description is sufficiently complete for an AI agent to decide when to use it and what to expect.

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% so the baseline is 3. The description goes beyond schema by explaining the 'language' parameter behavior in a non-obvious way: Thai entries are always available even when Thai is disabled, and the client may need to translate them. This adds meaningful semantic context to the 'language' parameter that the schema alone does not convey. However, it doesn't add details about 'word' or 'limit_context' beyond what the schema already states.

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 states the action ('Look up the dictionary meaning of a Pāli word') and the added value ('with sutta context'). It distinguishes the tool as a 'Pāli Dictionary Bridge' that pairs definitions with actual usage, which differentiates it from siblings like define_from_suttas and parse_pali_word. The specific verb+resource+scope structure makes its purpose immediately obvious.

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 does not provide explicit guidance on when to use this tool versus alternatives. It positions the tool as a 'Pāli Dictionary Bridge' but never mentions sibling tools, exclusions, or scenarios where another tool would be more appropriate. There is no 'when to use' or 'when not to use' guidance, leaving the AI agent to infer usage from the purpose alone.

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