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

Beyond the readOnlyHint and idempotentHint annotations, the description discloses the embedding model (BGE-base-en), similarity metric (cosine), windowing strategy (500-char overlapping), character cap (200K), and truncation flagging behavior. It also tells the agent that results include character offsets and similarity scores, enabling verbatim verification. This is rich behavioral detail beyond structured metadata.

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?

Each sentence serves a purpose: core action, examples, when-to-use, pairing with a sibling, and algorithm/limitations. The description is front-loaded with the primary function, and the technical details are positioned at the end. It is dense but not bloated, earning its length.

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 what the agent can expect (passages with offsets and similarity scores) and covers use cases, pairings, and operational limits (cap, truncation). It is sufficiently complete for an agent to decide when to invoke and what outcomes to anticipate, especially with the sibling context providing an ecosystem.

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 is 3. The description adds value by clarifying that 'text' must be already-fetched content and that inputs over 200K chars are truncated and flagged—details not fully captured in the schema. It also frames 'limit' as 'top-N passages,' reinforcing the default/range semantics though the schema already states this. This nudges above 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,' which is a specific verb+resource+scope statement. It names concrete inputs (SEC 10-K body, article, tool result) and clearly differentiates from sibling tools by highlighting that it searches within a provided text rather than a broader corpus, and explicitly pairs with ask_pipeworx_grounded.

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?

It explicitly states when to use: 'Use when the record is too big to cram into the prompt.' It explains the benefit (saves context, returns only relevant passages) and describes the complementary workflow with ask_pipeworx_grounded, which serves as an alternative grounding strategy. This is clear, actionable guidance.

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