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

Annotations already mark readOnly/openWorld/idempotent/non-destructive, but the description adds substantial context beyond those flags: account and paid-plan requirements, expected latency ranges, parallel decomposition behavior, explicit gaps[] instead of invention, contradiction reporting, citation_uri resolvability, and semantic excerpting of large records. No statement contradicts 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 long but front-loaded with the most decision-critical information (account requirement, alternative tool, not-open-web nature). Each major section earns its place, though there is minor redundancy with the schema (depth meanings) and the parenthetical 'one LLM call, not many' repeats an earlier point. Slightly tighter wording would make it perfect.

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 complex tool with no output schema, the description fully compensates: it describes the findings packet structure (verbatim evidence, confidence, source, fetched_at, citation), gaps[], contradictions[], hop fields, citation_uri behavior, latency, and auth constraints. An agent has everything needed to invoke it correctly and set user expectations.

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 depth parameter's schema description is already detailed, so a baseline of 3 applies. The description adds value with question examples ('compare X and Y', 'research the filings + market picture for ACME'), ties depth choices to latency expectations, and reinforces that broad/multi-part questions are acceptable. It enriches but doesn't transform what the schema provides.

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 clearly states a specific verb ('research'), resource (Pipeworx's 1517 structured data sources), and scope (multi-source, grounded, one call), and explicitly distinguishes itself from open-web search and from ask_pipeworx. It names what it is not, making sibling differentiation immediate even without opening other 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?

The description gives explicit when-to-use guidance (broad/multi-part questions over structured data) and when-not-to-use guidance (single lookup → ask_pipeworx, breaking/current news → ask_pipeworx, not signed in → ask_pipeworx). It also explains the tradeoffs between depth levels, so an agent can select the right variant confidently.

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