dedup_check

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

No annotations are provided, so the description carries full burden. It discloses the computational approach (computing content hashes for every chunk) and the output format (file pairs sorted by similarity), but does not explicitly state whether the operation is read-only or mention potential performance impact.

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 well-structured: two sentences covering purpose, method, output, and use case. Every sentence delivers value without redundancy.

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 simplicity (one parameter, no output schema), the description is fairly complete. It explains the return format at a high level and provides usage guidance. A minor gap is the lack of an explicit statement that the operation is read-only, but the description is otherwise 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?

The schema already provides a detailed description of the threshold parameter, including meaning, default, and range. The tool description adds no additional parameter semantics beyond what the schema provides, so the baseline score of 3 applies.

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 detects near-duplicate documents using content hashes and returns file pairs with high chunk overlap. It is specific about the verb (detect) and resource (documents in the index), and distinguishes itself from sibling tools like ingest_file or query_documents.

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 a clear use case: 'Use to identify accidentally duplicated or re-ingested content.' While it doesn't explicitly mention when not to use it or name alternatives, the context is sufficient given the tool's unique niche among siblings.

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