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

Although annotations already indicate read-only, idempotent, and open-world behavior, the description adds substantial technical disclosure: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, and a 200K-char cap with truncation flagging. It also clarifies that results include offsets for verification, which is valuable behavioral context beyond the annotations.

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 four sentences, each earning its place: purpose, use case, integration, and technical detail. It is front-loaded with the core function and avoids redundancy with the schema or annotations. No fluff or digressions.

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?

For a tool with no output schema, the description thoroughly explains what to expect: top-N passages, character offsets, and similarity scores. It also covers use cases, pairing with ask_pipeworx_grounded, and edge cases like truncation. Despite the absence of an output schema, the agent has enough context to invoke and interpret results correctly.

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 covers all three parameters at 100% coverage, so the baseline is 3. The description adds value by providing concrete examples for 'text' (SEC 10-K, article, tool result) and 'query' (supply-chain risk, fiscal year revenue), and it reinforces the character cap mentioned in the schema. This elevated semantics slightly above the baseline.

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 opens with 'Semantic search INSIDE a fetched record,' using a specific verb and resource. It clearly states inputs (text and natural-language query) and outputs (top-N passages with character offsets and similarity scores), and distinguishes itself from siblings by emphasizing it works on a previously fetched record rather than 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 provides explicit when-to-use guidance: 'Use when the record is too big to cram into the prompt.' It also explains how it pairs with ask_pipeworx_grounded for grounded generation. However, it does not list explicit when-not-to-use scenarios or alternative tools beyond this pairing, so it falls just short of a 5.

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