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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations by disclosing the TF-IDF algorithm and the fact that it works without external resources, which is useful for understanding what the tool does under the hood.

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 two concise sentences. The first sentence states the action and method; the second explains the purpose and avoids unneeded details. No wasted words.

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 low complexity, full schema coverage, output schema presence, and rich annotations, the description provides enough context. It explains what the tool does, how it does it, and when it's useful, which is complete for a read-only utility.

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 coverage is 100%, so all parameters are already described adequately in the schema. The description does not add extra parameter-level semantics, but the baseline of 3 applies because no additional meaning is needed beyond the schema.

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 what the tool does: 'Rank an array of text chunks by relevance to a query using TF-IDF scoring.' It also distinguishes itself from sibling tools by specifying the algorithm and noting it 'Simulates retrieval ranking for RAG testing without needing embeddings or an API.'

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 clear context: it is for RAG testing scenarios where embeddings or an API are unavailable. It does not explicitly name alternative tools or provide exclusion criteria, but the use case is well implied and focused.

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