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 discloses authentication requirements (IBM Cloud IAM credentials) and output format (structured array with document_id, metadata, passages). It does not mention side effects, but as a read operation this is acceptable.

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 well-structured with distinct sections (Description, Function, Use Cases, Authentication, Output Format). All sentences are relevant and front-loaded with purpose. It is slightly verbose but not excessive.

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 5 parameters, no output schema, and no sibling context issues, the description covers authentication and output format but lacks parameter details. Use cases are provided. It is partially complete but missing crucial per-parameter guidance.

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 0%, and the description does not explain individual parameters like project_id, collection_id (type array), count, or filter. The mention of natural language queries only hints at the natural_language_query parameter. The description does not compensate for the lack of schema documentation.

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 that the tool searches data using natural language queries for a specified project and collections, returning matching documents. It also mentions connection to IBM Watson Discovery, distinguishing it from sibling tools like get_projects and list_project_collections which are for listing.

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 'Use Cases' section provides two clear examples (searching documents, integration with workflows). It does not explicitly state when not to use or name alternatives, but the sibling tools imply the scope is for document search within a project, providing good context.

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