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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description goes well beyond annotations by disclosing: parallelism (5,798 tools, facets), response format (findings packet with verbatim evidence, confidence, source, fetched_at, citation), gap handling (gaps[] never invented), contradictions[] scan, semantic excerpting of large records, latency expectations (15-60s, up to 90s for thorough), and paid plan requirements. These are meaningful behavioral contexts that a caller needs.

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 information-dense but long and somewhat sprawling. It front-loads the account requirement, which is important context, but then packs many distinct behaviors and caveats into a dense paragraph. Every sentence adds value, but the structure could be improved with clearer separation of scope, behavior, and usage guidance. The length is justified by the tool's complexity, but it reads as a run-on block.

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 is remarkably complete: it explains the research approach, the outputs, the gap behavior, the citation format, the depth semantics, the important distinction from open-web search and ask_pipeworx, account requirements, and latency. Given the tool's complexity and that there is no output schema to fall back on, this is enough for an agent to call it correctly and interpret 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 baseline is 3. The description adds meaning beyond the schema by explaining the practical effects of the depth values: quick=3 facets single hop, standard=3 facets with gap-recovery plus contradictions scan, thorough=6 facets paid iterative hop plus contradictions scan. It also clarifies that question should be broad/multi-part in natural language. This exceeds baseline because it ties enum values to observable behavior and timing.

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 1,517 structured data sources in one call, and explicitly distinguishes itself from open-web search. It names what it is NOT and contrasts with ask_pipeworx for single lookups. This is a clear, differentiating definition.

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, and explicitly says for a single lookup use ask_pipeworx instead. It also covers account tier requirements, when deep_research returns gaps, and routes to ask_pipeworx if not signed in. This is strong usage guidance with alternatives and exclusions.

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