Get G2 software reviews. Returns ratings, pros, cons, use cases.
Args: product: Software product name (e.g. 'Salesforce') max_results: Max reviews (default 20)
| Name | Required | Description | Default |
|---|---|---|---|
| product | Yes | ||
| max_results | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, indicating a safe read operation. The description adds the specific output fields (ratings, pros, cons, use cases), which gives more detail than the annotations alone. It does not discuss limitations like pagination or rate limits, but for a simple read-only tool this is acceptable.
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 concise and well-structured: a single purpose sentence followed by a documented argument list. Every element contributes to understanding the tool without redundancy.
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?
Given the tool's simplicity (2 parameters, no output schema, read-only), the description covers all necessary aspects: what it does, what it returns, and how to pass arguments. No significant gaps remain.
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?
The description fully explains both parameters: 'product' is specified as a software product name with an example, and 'max_results' is described as max reviews with a default value. Since the schema has no descriptions (0% coverage), the description carries the full explanatory burden and does so effectively.
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 the tool retrieves G2 software reviews and specifies the return content (ratings, pros, cons, use cases). It uses a specific verb ('Get') and names the resource ('G2 software reviews'), which differentiates it from sibling tools like Trustpilot and Yelp.
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 when G2 reviews are needed, providing clear context. However, it does not explicitly mention alternatives or when not to use the tool, but for a single-purpose review fetcher, the context is sufficient.
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.