deduplicate_strings

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it uses Jina embeddings and submodular optimization to select semantically unique strings, returns a subset based on diversity, and automatically determines k if not provided. However, it doesn't mention performance characteristics like speed, rate limits, or error handling, which could be useful for an agent.

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 front-loaded with the core purpose in the first sentence, followed by usage guidelines and examples. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension by an AI agent.

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's moderate complexity (2 parameters, no output schema, no annotations), the description is largely complete. It covers purpose, usage, and behavioral aspects. However, without an output schema, it doesn't specify the return format (e.g., array of strings, scores), which is a minor gap. The description compensates well but isn't fully exhaustive.

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 schema description coverage is 100%, so the baseline is 3. The description adds value by explaining the semantic processing ('using Jina embeddings and submodular optimization') and the purpose of k ('to select the most diverse subset'), which goes beyond the schema's technical definitions. It also clarifies the automatic k behavior, enhancing parameter understanding.

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 with specific verbs ('get top-k semantically unique strings') and resources ('from a list using Jina embeddings and submodular optimization'). It distinguishes itself from siblings like 'deduplicate_images' by specifying it works on strings rather than images, and from 'sort_by_relevance' by focusing on semantic diversity rather than relevance ranking.

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 states when to use this tool ('when you have many similar strings and want to select the most diverse subset that covers the semantic space') and provides three specific use cases ('removing duplicates, selecting representative samples, or finding diverse content'). It doesn't mention alternatives, but given the sibling tools, none directly overlap with string deduplication, making the guidance complete.

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