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

The description goes well beyond the annotations by disclosing the algorithm (BGE-base-en embeddings + cosine), the windowing approach (500-char overlapping windows), the input cap (200K chars with truncation and flagging), and the output characteristics (passages with character offsets and similarity scores). This significantly enriches the agent's understanding of how the tool behaves.

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?

Every sentence in the description earns its place. The first sentence states the core function, the second gives the use case and benefit, the third provides a pairing with an alternative tool, and the fourth discloses technical constraints. There is no redundancy or filler; the description is dense but appropriately sized.

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?

With no output schema, the description adequately covers the return format by mentioning 'top-N passages with character offsets and similarity scores.' It also covers limitations (200K cap, truncation) and the intended workflow. The tool is simple enough, and the description gives all necessary context for an agent to understand its capabilities and boundaries.

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 the baseline is 3. The description adds meaningful context beyond the schema: it clarifies that 'text' should be a fetched record, explains the 200K character cap and truncation behavior, and reinforces the query as a natural-language question. These additions give a better sense of how to use each parameter effectively.

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,' which is a specific verb+resource combination. It immediately distinguishes itself from siblings by emphasizing that the text is already fetched and the search is conducted within that text, returning passages with offsets and scores. It even names the complementary tool ask_pipeworx_grounded, making the differentiation explicit.

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 an explicit 'Use when' condition: 'when the record is too big to cram into the prompt.' It also supplies a clear alternative workflow: 'Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document.' This gives both a positive trigger and a contrast with an existing tool.

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