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

Annotations already declare readOnly, openWorld, idempotent, non-destructive, and the description adds extensive behavioral detail: parallel decomposition routing, findings packet contents, gaps[] and contradictions[], citation_uri resolvability, semantic excerpting, latency, and account/plan requirements. This is far beyond what annotations provide and directly shapes agent expectations.

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 long but front-loaded with the most critical caveat (account required and fallback). Every major block—purpose, alternatives, depth behavior, output properties, latency—adds value, though a few concepts are repeated across the prose and schema, so it loses one point for not being tighter.

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 fully explains return values (findings packet, verbatim evidence, confidence, source, fetched_at, pipeworx:// citations, hop, gaps[], contradictions[]) and runtime expectations. It also covers prerequisites, plan gating, alternative tools, and failure modes (breaking news returns empty gaps), making the tool callable correctly in all relevant contexts.

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 description coverage is 100%, and the schema's parameter descriptions already explain depth levels and question phrasing. The tool description mostly repeats the depth semantics (gap recovery, contradiction scan, iterative hops) in prose without adding materially new parameter insight, so it stays at the baseline for high schema coverage.

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 states a precise action ('Grounded multi-source research') and a well-scoped resource (1497 STRUCTURED data sources including SEC, FRED, FDA, USPTO) and explicitly contrasts it with open-web search. It also names a sibling (ask_pipeworx) and explains the difference, so an agent can distinguish deep_research without opening schemas.

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 gives explicit when-to-use guidance ('Best for broad/multi-part questions over structured data'), when-not-to-use ('For a single lookup use ask_pipeworx'), and even an account-based routing rule ('If you are not signed in, use ask_pipeworx instead'). The alternative tool is named and its different behavior is described, leaving nothing to inference.

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