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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds technical details: embedding model BGE-base-en, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag, and output includes character offsets and similarity scores.

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 a single concise paragraph with front-loaded purpose, followed by usage guidance and technical details. Every sentence adds value with no redundant content.

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 output schema, the description explains return values (top-N passages with offsets and similarity scores), mentions truncation behavior, and pairs with sibling tool. All necessary context for a tool with 3 well-documented parameters is provided.

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 baseline 3. The description adds value through real-world examples for text (e.g., SEC 10-K body, article) and query (e.g., supply-chain risk), and explains the overall workflow, slightly exceeding 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 the verb 'Semantic search' and the resource 'INSIDE a fetched record'. It distinguishes from sibling tools by focusing on internal search within fetched data and explicitly mentions pairing with ask_pipeworx_grounded for grounding over passages.

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 says 'Use when the record is too big to cram into the prompt' and alternatives like ask_pipeworx_grounded are mentioned. It provides clear when-to-use guidance and implies when not to use.

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