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

The description adds extensive behavior beyond the readOnly/openWorld/idempotent annotations: account and paid-plan requirements, parallel decomposition, findings packet contents, explicit gaps[], never-invented behavior, fetchable citation URIs, contradictions[], semantic excerpting of large records, and latency expectations. 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 and dense, but the tool has complex behavior and no output schema, so most sentences earn their place. It is not optimally front-loaded, burying the core 'grounded multi-source research' definition after the account warning, and it partly repeats schema depth descriptions.

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 is unusually complete: prerequisites, alternatives, return format, citation semantics, gap handling, contradiction behavior, excerpting, and latency are all covered. An agent has enough context to invoke the tool correctly and interpret its results.

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 the baseline is 3, but the description adds meaningful context: depth:'thorough' requires a paid plan, standard/thorough perform gap-recovery and contradiction scans, and multi-step questions resolve in one call. It also gives realistic example questions that clarify what kind of natural language input is appropriate.

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 defines the tool as grounded multi-source research across 1,517 structured data sources in a single call, and explicitly says it is NOT open-web search. It also distinguishes itself from ask_pipeworx, making sibling differentiation clear without needing to inspect 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, with concrete examples, and explicitly directs single lookups to ask_pipeworx instead. It also states the signed-in prerequisite and the fallback for unsigned-in users, covering both selection conditions and exclusions.

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