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

Beyond the annotations (readOnlyHint, openWorldHint, destructiveHint=false), the description discloses data sources, keyless access, output structure (KPIs, signals, matrices), accuracy guardrails ('no hallucinated figures'), SLA times, and quality-score expectations for different target types. This adds substantial behavioral context not available from annotations alone.

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 dense but well-structured: it leads with the core value proposition, then covers data sources, output shape, reliability guarantees, SLA, use-case-specific guidance, and inputs. Every sentence provides distinct value, and the information is front-loaded with the most critical differentiators first.

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 multiple sources, nested output, and async variants, the description thoroughly covers what the tool does, what it returns, its limitations, its performance envelope, and how to choose between sync/async usage. The presence of an output schema further reduces the need to explain return fields, yet the description still summarizes the output structure helpfully.

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?

The input schema already has 100% description coverage, including detailed descriptions for company, competitors, and depth (with enum and timing). The description's parameter summary ('Inputs: company name or domain (required), optional competitor list (≤5), optional depth (easy/medium/hard)') adds no new semantic information beyond the schema, so the baseline of 3 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 opens with 'Gold-standard competitive deep dive — STRUCTURED multi-source data (no LLM narrative)' which clearly states the tool's purpose and output format. It explicitly distinguishes itself from the sibling tool 'competitor_intel' by contrasting structured data versus LLM-narrated briefings, making the differentiation strong.

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 provides explicit usage guidance: it names 'competitor_intel' as the alternative for narrative briefings, advises using sync for batch agents with ≤30s tolerance, and recommends 'competitive_deep_dive_async' plus result polling for conversational agents. It also notes performance expectations on different company types, giving clear when-to-use and when-not-to-use context.

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