Skip to main content
Glama

get_business_details

Resolve a single business to phone, address, website, hours, rating.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYes
business_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It only lists output fields but does not mention whether the operation is read-only, authentication needs, error behavior (e.g., what if business not found), or rate limits. Minimal transparency beyond the basic resolution action.

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?

The description is a single sentence of 10 words, which is very concise. The structure is simple and front-loaded with the key action. However, it omits important details, so conciseness comes at the cost of completeness, preventing a higher score.

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?

Given the low complexity (2 params) and presence of an output schema, the description could be minimal but still complete. However, it lacks usage guidelines, parameter semantics, and behavioral transparency, making it incomplete for an AI to use correctly. The agent would need to guess about error handling and exact input needs.

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

Parameters1/5

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

Schema coverage is 0%, meaning no parameter descriptions exist in the schema. The description should compensate but does not. It does not mention the required parameters (business_name and city) or explain their meaning, format, or constraints. The AI agent must infer from parameter names alone, which is insufficient.

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 'Resolve a single business to phone, address, website, hours, rating' clearly states the action (resolve) and the resource (single business) and lists specific output fields. It implicitly distinguishes from sibling tools like search_businesses (multiple results) and audit_website (specific URL audit), but does not explicitly differentiate.

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?

The description provides no guidance on when to use this tool versus alternatives. With siblings like search_businesses and audit_website, it would be helpful to mention that this tool is for a single business lookup or contrast with batch operations. No hints on prerequisites or typical use cases.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.