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

Annotations already establish that this is a read-only, non-destructive operation. The description adds useful behavioral context beyond that: recommendations are AI-ranked using profile, history, and active listings, and return a scored list of potential deals. It does not cover freshness, pagination, or detailed scoring behavior.

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 three sentences, front-loaded with the core purpose, then return behavior, then optional filters. Every sentence contributes useful information without waste.

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 read-only recommendation tool with full parameter documentation and annotations covering safety, the description is largely complete. Because there is no output schema, it could say more about the shape of the scored list, but it gives enough to understand and invoke the tool 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 description coverage is 100%, so the schema fully documents the parameters. The description echoes agentId and free_only filtering but does not add syntax or deeper meaning beyond the schema, so baseline 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 states a specific verb and resource: getting AI-ranked recommendations of offers and needs matched to the agent. It differentiates itself from raw search tools by tying results to profile, history, and active 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?

It implies the tool is for discovering recommended matches and mentions optional filtering, but it does not explicitly say when to use this instead of siblings like search_offers, search_needs, or discover_intents. The usage context is clear enough but alternatives are left to inference.

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