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

Annotations indicate idempotent, read-only, and non-destructive behavior. The description adds critical behavioral details: returns top-N passages with character offsets and similarity scores, uses BGE-base-en embeddings with 500-char overlapping windows, and caps input at 200K chars with truncation flagging.

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, well-front-loaded paragraph that efficiently conveys purpose, use case, benefits, pairing, and technical constraints without superfluous information.

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?

Despite lacking an output schema, the description explains return format (passages with offsets and scores), input limits, and embedding details. It provides all necessary context for correct agent invocation.

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 covers all parameters. Description adds meaningful examples for 'query', default and range for 'limit', and reiterates the 200K char limit for 'text', enhancing understanding 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 the tool performs semantic search inside a fetched record, specifying the use case when records are too large for prompts. It differentiates from siblings like ask_pipeworx_grounded by highlighting that it searches inside provided text, not external sources.

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 advises using this tool when a record is too large for the prompt and mentions pairing with ask_pipeworx_grounded for grounded reasoning. While it doesn't explicitly state when not to use it, the positive guidance is strong and provides context.

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