G2 Reviews API Server

The most efficient, reliable, and developer-friendly way to use the G2 Reviews API.

Actor page: apify.com/johnvc/g2-reviews-api Input schema: apify.com/johnvc/g2-reviews-api/input-schema

Give it one or more G2 product review URLs and it returns one clean JSON row per review: rating, title, pros, cons, reviewer role, company size, and the publish date. Optionally add a product-metadata row per product with category, star rating, review count, and competitors. It is built API-first and MCP-ready, so you can call it from Python or drive it as a tool from an AI agent.

Video Walkthrough

Watch the walkthrough

Quick Start

Prerequisites

• Python 3.11 or higher
• An Apify account and API key (get a free key here)

1. Clone the repository

   git clone https://github.com/johnisanerd/Apify-G2-Reviews-API.git
   cd Apify-G2-Reviews-API
   

2. Install dependencies with UV

   # Install UV if you do not have it:
   curl -LsSf https://astral.sh/uv/install.sh | sh
   
   # Install project dependencies:
   uv sync
   

3. Configure your API key

   cp .env.example .env
   # Edit .env and add your Apify API key
   # Get your free API key at: https://apify.com?fpr=9n7kx3
   

4. Run the example

   uv run python g2-reviews-api-example.py
   

Alternative: set the API key directly

export APIFY_API_TOKEN="your_api_key_here"
uv run python g2-reviews-api-example.py

Why Use This G2 Reviews API?

A URL in, structured data out. You never touch collection infrastructure. Pass one or more G2 product review URLs and get flat, predictable fields you can load straight into a sheet, a database, or a BI tool.

One row per review. Every review comes back with the same field shape: rating, title, pros, cons, reviewer role, company size, and the date it was published, plus a plain-language summary line.

Pay per review. Billing is per review returned, with no per-run setup fee, so you only pay for what is delivered. The maxReviewsPerProduct cap lets you control both volume and cost.

Batch a whole competitive set. Send many product URLs in one run to compare ratings and sentiment across products, by reviewer role and company size.

Optional product metadata. Turn on includeProductMetadata to add one product-level row per product, with category, star rating, review count, and competitors.

Reliable and predictable. A product with no reviews returns a clear message instead of failing the whole run, and a URL that cannot be collected returns an error row so one bad link never sinks the batch.

MCP-ready. Call it as a tool from Claude, Cursor, and other AI agents (see the install sections below).

Features

Core Capabilities

• Collect reviews from one or many G2 product review URLs (up to 100 per run)
• Cap reviews per product with maxReviewsPerProduct to control volume and cost
• Sort by most recent, most helpful, highest rated, or lowest rated
• Optional per-product metadata row with category, star rating, review count, and competitors

Data Quality

• One consistent JSON row per review, every time
• A plain-language summary field on every review for quick scanning and AI use
• Clear error rows for URLs that cannot be collected, so a batch never fails as a whole

Usage Examples

Reviews for one product

{
  "productUrls": ["https://www.g2.com/products/asana/reviews"],
  "maxReviewsPerProduct": 5
}

Several products, most recent first, capped

{
  "productUrls": [
    "https://www.g2.com/products/asana/reviews",
    "https://www.g2.com/products/trello/reviews"
  ],
  "maxReviewsPerProduct": 200,
  "sortBy": "recent"
}

With product metadata

{
  "productUrls": ["https://www.g2.com/products/asana/reviews"],
  "maxReviewsPerProduct": 50,
  "includeProductMetadata": true
}

Input Parameters

Parameter	Type	Required	Default	Description
productUrls	list[str]	YES	-	One or more G2 product review URLs, for example https://www.g2.com/products/asana/reviews. A plain product URL without /reviews is accepted and normalized. Up to 100 per run.
maxReviewsPerProduct	int	No	100	Maximum reviews to return per product (1 to 1000). Caps cost and volume; each product is capped independently.
sortBy	str	No	""	Sort order for reviews. Empty for the default (most relevant), or one of recent, helpful, highest, lowest.
includeProductMetadata	bool	No	false	When enabled, add one product-metadata row per product (category, star rating, review count, competitors). Billed as a separate product-metadata event.

Output Format

Each review is returned as one JSON row:

{
  "result_type": "review",
  "productName": "Asana",
  "rating": 4.5,
  "title": "Simple, team-friendly interface that keeps everyone productive",
  "reviewerRole": "Program Manager",
  "companySize": "Small-Business (50 or fewer emp.)",
  "datePublished": "2026-07-06",
  "summary": "4.5-star verified review of Asana from Program Manager: \"Simple, team-friendly interface\"",
  "verified": true,
  "reviewerName": "Jordan M.",
  "pros": "The interface is simple enough to learn quickly.",
  "cons": "More automations would be helpful in all plans.",
  "reviewUrl": "https://www.g2.com/products/asana/reviews/asana-review-13068232"
}

With includeProductMetadata enabled, each product also yields one metadata row:

{
  "result_type": "product_metadata",
  "productName": "Asana",
  "productUrl": "https://www.g2.com/products/asana/reviews",
  "category": "Project Management",
  "starRating": 4.4,
  "reviewCount": 11000,
  "competitors": [{ "name": "Trello" }, { "name": "monday.com" }]
}

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Install in Claude Cowork Desktop

Cowork is the desktop app's automation mode. To give it the G2 Reviews API as a tool, add the Apify MCP server as a connector.

1. Open the Claude desktop app and go to Settings → Connectors (or Settings → Developer → Edit Config to edit claude_desktop_config.json directly).
   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json
2. Add the Apify MCP server, preloaded with only this Actor:

{
  "mcpServers": {
    "apify": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mcp.apify.com/?tools=actors,docs,johnvc/g2-reviews-api"
      ]
    }
  }
}

3. Restart the app. When Cowork first calls the tool, complete the OAuth prompt in your browser, or add your Apify API token in the connector settings to skip OAuth.
4. In a Cowork chat, confirm the tool is available and ask it to run the G2 Reviews API.

Download the desktop app and start a free trial: https://claude.ai/referral/uIlpa7nPLg More help: https://docs.apify.com/platform/integrations/claude-desktop

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Install in Claude Code

Claude Code is the command-line tool. Add the Actor's MCP server with one command:

claude mcp add --transport http apify \
  "https://mcp.apify.com/?tools=actors,docs,johnvc/g2-reviews-api"

To use a token instead of browser OAuth:

claude mcp add --transport http apify \
  "https://mcp.apify.com/?tools=actors,docs,johnvc/g2-reviews-api" \
  --header "Authorization: Bearer YOUR_APIFY_TOKEN"

Then verify with claude mcp list, or run /mcp inside a session. Ask Claude Code to call the G2 Reviews API.

Try Claude Code free: https://claude.ai/referral/uIlpa7nPLg Claude Code MCP docs: https://code.claude.com/docs/en/mcp

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Install in Claude (website)

On claude.ai you add Apify as a connector, then enable just this Actor's tool.

1. Go to Settings → Connectors → Browse connectors and search for Apify MCP server. Install it (enable or update if prompted).
2. When connecting, authenticate with your Apify API token, and enable the tool johnvc/g2-reviews-api.
3. In any chat, open + → Connectors and turn on Apify.
4. Alternatively, choose Add custom connector and paste the full MCP URL https://mcp.apify.com/?tools=actors,docs,johnvc/g2-reviews-api, using OAuth when prompted.
5. Ask Claude to run the G2 Reviews API.

Open Claude on the web: https://claude.ai

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Install in Cursor

Cursor reads MCP servers from a project file at .cursor/mcp.json.

1. In your project, create .cursor/mcp.json:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=actors,docs,johnvc/g2-reviews-api"
    }
  }
}

2. If you prefer token auth over browser OAuth, add a header:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=actors,docs,johnvc/g2-reviews-api",
      "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }
    }
  }
}

3. Open Cursor → Settings → MCP and confirm the apify server is connected (green dot).
4. In Composer or Chat, ask Cursor to call the G2 Reviews API.

New to Cursor? Get it here: https://cursor.com/referral?code=XQP4VBLI3NNX

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Install in ChatGPT

ChatGPT connects to the Apify MCP server through Developer mode (available on ChatGPT Pro, Plus, Business, Enterprise, and Education plans).

1. Click your profile icon, then go to Settings > Apps. If you do not see a Create app button, open Advanced settings and enable Developer mode.
2. Click Create app and fill out the form:
   • Name: Apify
   • MCP Server URL: https://mcp.apify.com/?tools=actors,docs,johnvc/g2-reviews-api
   • Authentication: OAuth
3. Click Create and authorize the connection with Apify.
4. To use the app in a conversation, click + in the chat, choose Developer mode, and select Apify.

More help: https://docs.apify.com/platform/integrations/mcp

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Made with care

Use the G2 Reviews API to power your competitor analysis, customer sentiment, and review monitoring with reliable, structured results.

Last Updated: 2026.07.11