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

Beyond the read-only/idempotent annotations, the description discloses substantial behavioral traits: account requirements and paid depth tier, parallel decomposition across 5,724 tools, findings packet shape, explicit gaps[] for unanswered facets, contradictions[] for deeper depths, citation_uri fetchability, semantic excerpting, and expected latency. This is far more than annotations alone 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 the tool's complexity justifies it, and nearly every clause supplies operational detail an agent needs. It front-loads the account requirement and the key alternative, though it is dense enough that splitting into short paragraphs would improve scannability.

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?

With no output schema, the description fully covers what to expect: findings packet contents, gaps[] semantics, contradictions, hop fields, fetchable citations, excerpting behavior, and runtime. It also covers account/tier prerequisites and when results will be empty. An agent has enough context to invoke the tool and interpret its response correctly.

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 genuine meaning beyond the schema: it explains what depth levels do behaviorally (gap recovery, chasing leads, contradiction scans) and clarifies that broad/multi-part questions are acceptable because decomposition is the tool's purpose. The question parameter gets contextual guidance beyond its schema text.

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 1497 STRUCTURED data sources' in one call, and explicitly says 'this is NOT open-web search.' It distinguishes itself from siblings by emphasizing broad/multi-part structured-data questions, so an agent can tell it apart from ask_pipeworx and related tools.

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 names alternatives with conditions — use ask_pipeworx for single lookups, and use ask_pipeworx for breaking/current-news topics because deep_research returns gaps. It even covers account-tier routing, making the decision path fully explicit.

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