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Bezal — Local Business Intelligence for AI Agents

check_business_availability

Check whether a business is open on a date, based on its posted opening hours. Hours are shared only for providers on a paid Bezal plan, and other listings come back with open set to "unknown". It doesn't know about bookings, so confirm a time with the business.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesISO date (YYYY-MM-DD)
business_idYesBusiness UUID

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / date / pattern
      Added value: +"^\\d{4}-\\d{2}-\\d{2}$"
  2. Added

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It honestly discloses two key behavioral limitations: hours are only available for paid-plan providers, and the tool is unaware of bookings. This is valuable context beyond the schema, though it does not describe the exact response structure beyond the 'open' field.

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?

Three sentences with no filler: the core action is front-loaded, followed by the critical plan limitation and the booking caveat. Every sentence provides necessary information for correct use.

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

Completeness4/5

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

For a simple two-parameter lookup with no output schema, the description covers the essential context: what the check is based on, when the result is unreliable, and how to interpret the outcome. It could additionally state the full expected output shape, but this is a minor gap.

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 coverage is 100%, so the two parameters are already fully documented with formats and patterns. The description references 'a date' and 'the business' generically but adds no parameter-specific semantics beyond what the schema provides, matching the baseline of 3.

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 uses a specific verb and resource ('Check whether a business is open on a date') and adds the important scoping detail 'based on its posted opening hours'. It is clearly distinct from sibling tools like get_business, but it does not explicitly name or contrast an alternative, so it stops short of full sibling differentiation.

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

Usage Guidelines4/5

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

The description provides clear context on when results are reliable (paid Bezal plan) and when they are not (other listings return 'unknown'), plus a warning that it does not account for bookings. It implies usage boundaries but does not explicitly name a fallback or alternative tool, so it earns a 4 rather than a 5.

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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