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recommend

Generate a recommendation set for a tenant based on a natural-language query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural-language recommendation request
tenantYesMerchant domain, e.g. demo.suggestapi.com

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries the full burden but only states that it generates a recommendation set. It does not disclose whether the operation is read-only, whether it has side effects, any authentication requirements, or what the output structure looks like.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single clear, front-loaded sentence that states the action and scope with no unnecessary detail. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter tool with full schema coverage, the description is minimally viable. However, it omits usage context and behavioral details such as the expected output shape or when to prefer a sibling tool, leaving the agent to infer some necessary context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and both parameters already have meaningful descriptions. The tool text adds minimal extra meaning beyond framing query as a natural-language request and tenant as the scoping domain.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool's function: generating a recommendation set from a natural-language query for a tenant. It is distinct in resource and intent from sibling tools like search and compare, though it does not explicitly name them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus sibling tools such as search, compare, or product_lookup. There are no conditions, exclusions, or alternative references in the description.

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

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