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BACH-AI-Tools

bach-yelp_business_api

Upcheck

upcheck

Check if the Yelp Business API is live and operational before making requests to avoid failures.

Instructions

Check if the api status is live!

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
checkYesExample value: true

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden, and it discloses almost nothing: no auth requirements, no indication of latency or whether the call is cheap versus rate-limited, and no statement of what happens on failure. A status probe is implicitly read-only, but that is inference, not disclosure. The only real signal is the word 'Check', which suggests a non-mutating operation.

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

Conciseness4/5

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

A single short sentence with the action front-loaded; nothing is padded or repeated. The informal exclamation mark slightly undercuts the professional register, but the size is appropriate.

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

Completeness2/5

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

With no annotations and no output schema, the description is the only source of information about this tool, and it omits both the response shape (boolean? status string? latency?) and the meaning of the required 'check' parameter. For a one-parameter probe this is a small surface, but the agent still cannot form a correct call or interpret the result.

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

Parameters2/5

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

Schema description coverage is nominally 100%, but the sole parameter's schema text is the unhelpful 'Example value: true' for a string field named 'check' - it neither explains what to pass nor reconciles the type/example mismatch. The description adds nothing about this parameter, so the agent is left guessing what 'check' means. Nominal coverage does not compensate here because the schema content is not actually informative.

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 states a concrete verb and resource: 'Check if the api status is live.' An agent can tell this is a health/status probe rather than a data-fetch tool, which is enough to place it apart from siblings like 'business_details' or 'get_menus_beta'. It loses a point for vagueness ('the api' - which API? 'live' - uptime, latency, cached?) and for naming no sibling it excludes.

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?

There is no statement of when to call this tool, what precedes it, or what alternative to use instead. It reads as an unconditional call with no context about preconditions or timing. The only implied usage is 'when you want to know if the API is up.'

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