Look up a word

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns user-specific context if cards exist, with fallback to a shared lexicon. It also lists the types of information (translation, gender, CEFR, FSRS state). This is reasonably transparent, though it does not specify read-only status or error conditions.

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 two sentences with no wasted words. The first sentence defines the tool's function and scope, and the second provides a clear usage directive. Information is front-loaded and efficient.

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 has 3 parameters, no output schema, and no annotations, the description covers the essential aspects: what the tool does, what it returns (user context with fallback), and when to use it. It does not explain the output format or error handling, but for a straightforward lookup tool this is sufficient.

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 does not add new parameter-level details beyond what the schema provides. However, it adds overall context about what the parameters are used for (e.g., looking up a word with personal context), which is helpful but does not extend the semantic meaning of individual parameters.

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 verb 'look up' and the resource 'one word', and specifies that it returns the user's personal context (translation, gender, CEFR, FSRS state) with a fallback to a shared lexicon. This distinguishes it from sibling tools like search_examples (which searches through examples) and get_vocabulary (which retrieves a list).

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 explicitly tells when to use the tool: 'to ground an LLM's guesses about a word rather than inventing them.' It provides clear context but does not explicitly mention when not to use it or list alternatives, which prevents a score of 5.

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