Get Yelp business reviews. Returns ratings, review text, photos.
Args: business: Business name or type (e.g. 'pizza') location: City (optional, e.g. 'Chicago') max_results: Max results (default 20)
| Name | Required | Description | Default |
|---|---|---|---|
| business | Yes | ||
| location | No | ||
| max_results | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint and openWorldHint, so the safety profile is already known. The description adds context about return fields (ratings, review text, photos) and max_results default, which are behavioral details not present in the annotations. No contradictions detected.
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 compact and front-loaded with the main purpose, followed by a clear, well-formatted argument list. No redundant or unnecessary information is present; every sentence contributes to understanding the tool.
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?
With no output schema, the description mentions return fields but does not specify whether the result is a list or a single review, nor the overall structure. The max_results parameter implies a list, but this could be explicit. Overall, it's reasonably complete for a simple read-only search tool.
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?
All three parameters are fully documented in the description with examples and defaults, compensating for the 0% schema description coverage. This goes beyond the schema by explaining what 'business' means (name or type), giving example values, and specifying the default for max_results.
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 clearly states 'Get Yelp business reviews' with a specific verb and resource, and lists return contents (ratings, review text, photos). This effectively differentiates from sibling tools like g2_reviews and trustpilot_reviews by naming the platform.
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?
The description implies usage through the 'business' and 'location' parameters, but it doesn't explicitly state when to use this tool over alternatives (e.g., 'use for Yelp reviews only'). No exclusions or alternative tool references are provided, leaving the choice somewhat implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct review platform (G2, Trustpilot, Yelp) with platform-specific arguments (product, company, business+location). There is no ambiguity about which tool to use for a given source.
All tool names follow the same {source}_reviews pattern with lowercase source names. This creates a predictable and uniform naming convention across the set.
Three tools is a reasonable scope for a review-intelligence server covering three major review platforms. Each tool has a clear purpose and the count is within the ideal 3-15 range.
The set provides read-only review retrieval for three key platforms, which covers the core domain. Minor gaps like additional platforms (e.g., Google, Amazon) or cross-platform search exist but do not undermine basic functionality.