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

Annotations already signal a safe read operation, and the description adds substantial behavioral context: account/paid-tier requirements, parallel decomposition across 5,798 tools, output packet contents (verbatim evidence, confidence, source, fetched_at), explicit gaps for unanswered facets, contradiction scans, fetchable citation_uri, semantic excerpting, and 15-90s latency. Nothing contradicts the readOnly/openWorld/idempotent hints.

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 it is dense and organized: auth caveat first, then core purpose, then routing guidance, then depth behavior, output details, and latency. A few parenthetical asides and repeated ask_pipeworx comparisons add slight redundancy, but no sentence is pure filler.

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 supplies a thorough return contract: findings packet fields, gaps[], contradictions[], hop field, citation_uri, and fetchability guarantees. It also covers prerequisites, pricing, alternatives, failure mode on non-structured topics, and timing, so an agent has enough context to invoke and interpret the tool 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?

Schema coverage is 100% and the schema already explains the depth enum, so the description does not need to carry the parameter documentation. It does add useful parameter-adjacent context: 'thorough' requires a paid plan, and expected latency varies by depth, which an agent can use to set user expectations.

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 specific verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources' in one call, and immediately contrasts it with open-web search and ask_pipeworx. It also gives representative queries ('compare X and Y', 'research the filings + market picture for ACME'), which makes the tool's role unmistakable.

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') and explicit exclusions: use ask_pipeworx when not signed in, for a single lookup, and for breaking/current news. This is enough to route an agent correctly among the many siblings.

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