generate_sample_message

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

No annotations provided, so description carries full burden. Adds critical '[Premium]' access constraint and 'realistic' quality indicator, plus boundary constraints (supported message types list). However, omits error behavior for unsupported types, idempotency, quota/credit consumption, and output format details.

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?

Single sentence with zero waste. Front-loads '[Premium]' status and main verb, follows with supported types list. Every element earns its place; no filler text despite high information density.

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?

Appropriate for a 2-parameter tool without output schema. Covers core functionality, access level, and input constraints. Missing only error handling details and output structure description, which would be nice-to-have given the 'Premium' flag.

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 has 100% description coverage (baseline 3), but description adds significant value by enumerating all supported message_type values (ADT^A01 through DFT^P03) which the schema doesn't constrain as enums. This specific list helps agents select valid inputs.

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?

Clear specific verb ('Generate') + resource ('sample HL7 messages') + context ('for testing'). The exhaustive list of supported message types (ADT^A01, ORM^O01, etc.) effectively distinguishes this from siblings like parse_hl7_message and validate_hl7_message.

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?

Provides implied usage context ('for testing') but lacks explicit when-to-use/when-not-to-use guidance or named alternatives. Doesn't clarify relationship to generate_mirth_channel or when to use real vs. sample data.

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