find_similar_elements

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

No annotations are provided, so the description carries the full burden. It mentions the scoring methods (Jaccard similarity and Shannon entropy) and that it returns 'similarity scores and relationships', but lacks details on performance (e.g., speed, rate limits), side effects, or error handling. For a tool with no annotations, this leaves significant behavioral gaps.

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, stating the core purpose in the first sentence. The second sentence adds useful return information without redundancy. It could be slightly more structured (e.g., bullet points), but it's efficient with minimal waste.

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 no annotations and no output schema, the description is moderately complete. It covers the purpose and return values but lacks details on behavioral traits (e.g., whether it's read-only, performance constraints) and doesn't fully compensate for the absence of structured output information. For a tool with 4 parameters and complex NLP operations, more context would be beneficial.

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 fully documents all parameters. The description adds no additional meaning beyond the schema, such as explaining how 'element_name' is used in similarity calculations or the practical impact of 'threshold'. Baseline 3 is appropriate as the schema does the heavy lifting.

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: 'Find elements that are semantically similar to a given element using NLP scoring (Jaccard similarity and Shannon entropy).' It specifies the verb ('find'), resource ('elements'), and method ('NLP scoring'), though it doesn't explicitly distinguish from sibling tools like 'search_all' or 'search_collection' which might have overlapping functionality.

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 provides no guidance on when to use this tool versus alternatives. With many sibling tools like 'search_all', 'search_collection', and 'search_by_verb', there's no indication of how this tool differs in context or when it's preferred, leaving the agent to guess based on the name alone.

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