suggest_words

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

With no annotations, the description carries the burden and does a good job: it discloses the ranking heuristic, the opening-word fallback, and the subtle duplicate-letter handling. This is substantive behavioral context that goes beyond the name and schema.

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 concise and front-loaded with the core purpose. Each sentence adds meaningful detail—usage, ranking, and an important edge case—without waste or redundancy.

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 there is no output schema and no annotations, the description covers the essential context: what the tool does, how to pass inputs, ranking behavior, no-guess behavior, and duplicate handling. It doesn't mention what happens if no words match or the exact return format, but these are minor gaps for a suggestion tool.

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 0% for top-level parameters, so the description compensates by explaining what 'guesses' should contain (all guesses plus tile colors) and how feedback is interpreted. It does not address 'max_results', but that parameter is relatively self-explanatory.

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 tool's primary function: returning five-letter words consistent with prior guesses. It distinguishes itself from siblings by focusing on guess-consistency, while the no-guess fallback to opening words is clearly described.

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 gives explicit instructions on when to use the tool: pass every guess with the tile colors Wordle showed. It also clarifies behavior when no guesses exist, but it doesn't explicitly mention alternatives like 'starting_words' or 'validate_word' for comparison.

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