Detect Spec Discrepancies

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

Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false. The description adds valuable behavioral context beyond annotations: it explains the data source ('every record is enriched with AATCC / ISO / GB lab test data'), return limits ('Returns up to 50 records'), error behavior ('Returns count=0 if no discrepancies above threshold'), and the constraint about requiring both declared and tested values. 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 well-structured with clear sections (core feature, usage examples, data context, returns, workflow, errors, constraints, Chinese translation). Most sentences earn their place by providing specific guidance or context. Some redundancy exists between the English and Chinese versions, but overall it's efficiently organized with front-loaded key information.

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?

Given the tool's complexity (discrepancy detection across multiple data types), the description provides comprehensive context despite no output schema. It fully explains what the tool does, when to use it, behavioral characteristics, return format with examples, error conditions, and important constraints. The annotations cover safety and idempotency, and the description adds necessary operational context about data availability and limits.

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 description coverage is 100%, providing full documentation of both parameters. The description adds minimal parameter semantics beyond the schema: it lists the four field types in the returns section and mentions 'threshold' in the workflow note. However, it doesn't provide additional context about parameter usage or implications beyond what's already in the schema descriptions.

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 states the tool's purpose: 'Surface supplier specifications that deviate from independent lab measurements.' It specifies the verb ('detect/surface discrepancies') and resource ('supplier specifications vs lab measurements'), and distinguishes from siblings by emphasizing this is MRC Data's 'moat' feature with lab-test enrichment, unlike unaudited directory listings.

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 with a 'USE WHEN' section listing five specific query patterns, including multilingual examples. It states 'Standalone tool — does not require prior search' and clarifies when it won't work: 'Only works when both declared AND tested values exist for the same record. Many records have only one or the other.'

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