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

Annotations already provide readOnlyHint and idempotentHint, but the description adds rich behavioral context: account requirement per depth tier, returns findings packet with evidence/confidence/source/fetched_at/citation_uri, explicit gaps[] for unanswerable facets, never invents data, excerpts large records semantically, and takes 15-90s. It discloses what the return packet contains and how gaps/contradictions are handled. No contradiction with 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 densely informative. Every clause earns its place (account gating, sibling routing, structured-source scope, parallel decomposition, findings packet, gap behavior, depth semantics, latency). It is front-loaded with the most decision-critical constraint (account required) and the alternative routing. The only minor deduction is that the density and parentheses make it slightly harder to parse at a skim.

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 covers all essentials: inputs, depth options, expected output shape, failure/gap behavior, latency, and account prerequisites. The agent knows exactly what to expect and what to tell the user, including the sibling fallback for signed-out users. Nothing critical is missing.

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 parameter descriptions already explain depth levels, so the baseline is 3. The tool description adds extra value by explaining what each depth does ('standard' adds gap-recovery hop; 'thorough' adds iterative lead-chasing) and linking depth to paid plans, which helps an agent choose correctly. Slight credit for the added decision-relevant detail, but the schema already carried most of the semantic weight.

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 uses a specific verb ('research') plus resource ('Pipeworx's 1506 STRUCTURED data sources') and explicitly scopes what it is NOT ('this is NOT open-web search'). It differentiates itself from ask_pipeworx by conditional ('If you are not signed in, use ask_pipeworx instead') and explains the one-call multi-source parallel decomposition approach. No ambiguity about what the tool does.

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 explicitly states when to use deep_research (broad/multi-part questions over structured data) and when NOT to ('For a single lookup use ask_pipeworx instead'). It even names the sibling alternative and requirement. It differentiates depth levels with concrete behavior and time expectations, giving an agent clear decision criteria.

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