spell_suggest

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

With no annotations, the description carries the full burden. It discloses the internal two-stage algorithm, which gives insight into performance and behavior. However, it does not mention that the tool is read-only (likely true) or any auth/rate limit requirements. The transparency is good but incomplete.

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 extremely concise with two sentences that front-load the purpose and algorithm. Every word earns its place, with no redundancy or extraneous information.

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?

The algorithm is well-explained, but there is no mention of the output format or return values. Since there is no output schema, the description should cover what the tool returns (e.g., list of suggestions with scores). This gap prevents the description from being fully complete.

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 100%, so the schema already documents all four parameters with descriptions. The tool description adds no additional meaning beyond what is in the schema, hence a baseline score of 3 is appropriate.

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 purpose: 'Suggest close matches for a (potentially misspelled) query over the vocabulary of entity names + tag values.' It distinguishes from siblings by detailing the two-stage algorithm (bigram-Jaccard + Levenshtein) and specifying the scope (entity names + tag values), which differentiates it from other search tools like fuzzy_search or search_nodes_ranked.

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?

There is no guidance on when to use this tool versus other similar tools such as fuzzy_search or search_nodes_ranked. The description does not provide context for when spell correction is preferable or any exclusions.

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