dictionary_semantic_neighbors

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

With no annotations provided, the description carries the full behavioral disclosure burden and does so well. It reveals that the tool depends on an installed memory-mapped Numberbatch ANN index, returns an empty unavailable response when artifacts are missing, and returns finite cosine similarity in the range -1 through 1. These are genuinely useful operational details beyond minimal expectations.

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 compact and well-structured: a purpose-led opening, a dependency note, parameter behavior, and output semantics. Each sentence earns its place without redundancy, and critical information is front-loaded.

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 that an output schema exists and the annotation set is empty, the description covers the key operational caveat (unavailable artifacts), the language-filter behavior, and the similarity semantics. The agent has enough context to invoke the tool correctly and interpret its behavior.

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?

The description adds meaningful parameter semantics beyond the schema, especially for target_language and min_similarity, by explaining global, monolingual, and cross-lingual modes and the similarity range. Some parameters like limit and source_language still rely mostly on schema descriptions, but the schema already covers their defaults and constraints reasonably well.

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 opens with a clear verb and resource: 'Find distributional semantic neighbours with an optional language filter.' This precisely identifies the tool's function and its distinguishing use of a Numberbatch ANN index. It does not explicitly contrast itself with siblings like dictionary_synonyms or dictionary_relations, so it falls just short of full differentiation.

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 concrete usage guidance for the language filter: omit target_language for global multilingual results, set it equal to source_language for monolingual results, or use a different tag for cross-lingual results. This is clear and actionable, though it does not explicitly state when to choose this tool over sibling tools.

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