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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses rich behavioral details: it decomposes questions, routes to tools in parallel, returns gaps[] for unanswered facets, never invents answers, provides contradictions[] scans for standard/thorough, returns citation_uri only when fetchable, semantically excerpts large records, and gives expected latency. This greatly exceeds what annotations provide.

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 every sentence adds distinct value: auth prerequisites, fallback routing, tool scope, parallel execution, output packet contents, gap handling, latency, and depth-specific behaviors. The most important constraint (account required and fallback) is front-loaded, and the structure is dense without redundancy.

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 research tool with no output schema, the description fully covers what an agent needs: input examples, depth semantics, return packet structure, evidence and citation format, failure/gap behavior, latency expectations, and explicit alternatives. The description is complete enough to invoke the tool correctly without external knowledge.

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 some contextual color around depth (e.g., latency implications, paid plan for thorough, contradiction behavior), but the schema already documents the key parameter semantics for both question and depth, including the quick/standard/thorough distinctions. The description does not materially change the agent's understanding of the parameters.

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 and resource: grounded multi-source research over Pipeworx's 1,517 structured data sources in one call. It explicitly distinguishes itself from open-web search and from sibling tools like ask_pipeworx, noting that ask_pipeworx is the alternative for single lookups and for users without an account.

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: best for broad/multi-part questions over structured data. It also states when not to use it—for a single lookup, use ask_pipeworx—and gives a concrete fallback if the user is not signed in. These exclusions and alternatives are unambiguous.

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