CompareFairly MCP Server

CompareFairly MCP Server

Provides AI agents with real-time access to neutral, sourced comparisons across products and services in multiple languages and countries, with tools for searching, fetching detailed comparisons, and listing categories.

Category
Visit Server

README

CompareFairly MCP Server

Model Context Protocol server for CompareFairly - a universal, AI-driven comparison engine. It gives AI agents real-time access to neutral, sourced comparisons across any category (products, services, and more), in multiple languages and countries.

Runs as a stdio server (dependency-free bridge) or can be called directly as a remote HTTP endpoint.

  • Remote endpoint: https://comparefairly.com/mcp (Streamable HTTP, JSON-RPC 2.0)
  • Auth: none (public, read-only) · CORS: open · Rate-limited per IP

Why

Comparison is one of the top things people (and now agents) use AI for, but most comparison content is opinion or pay-to-rank. CompareFairly is built on three principles that make it a trustworthy data source for agents:

  • Provable neutrality - ranking is never bought; sorted on measured criteria.
  • Traceability - every value links to a source; "sourced" vs "estimate" is explicit.
  • Freshness - each comparison exposes its data age and TTL; stale data is flagged.

Tools

Tool Description
search_comparisons Search the catalog. Params: query, lang, country, limit.
get_comparison One full structured comparison (scores, sub-scores, per-criterion values, badges, sources, freshness). Params: slug, lang, country.
list_categories Top-level categories for a market. Params: lang, country.

A comparison exists per market: always pass the same lang + country you got from search_comparisons. If a slug exists only in other markets, get_comparison returns available_markets so the agent can retry correctly.

Install

Option 1 - stdio via npx (no install)

npx -y github:cgdev2019/comparefairly-mcp

Client config:

{
  "mcpServers": {
    "comparefairly": {
      "command": "npx",
      "args": ["-y", "github:cgdev2019/comparefairly-mcp"]
    }
  }
}

Option 2 - Docker

docker build -t comparefairly-mcp .
docker run --rm -i comparefairly-mcp
{
  "mcpServers": {
    "comparefairly": {
      "command": "docker",
      "args": ["run", "--rm", "-i", "comparefairly-mcp"]
    }
  }
}

Option 3 - remote HTTP (no local process)

{
  "mcpServers": {
    "comparefairly": {
      "type": "streamable-http",
      "url": "https://comparefairly.com/mcp"
    }
  }
}

Requires Node >= 18 for the stdio options. Override the backend with COMPAREFAIRLY_MCP_URL if needed.

Raw calls

# List tools
curl -s -X POST https://comparefairly.com/mcp \
  -H 'content-type: application/json' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

# Search
curl -s -X POST https://comparefairly.com/mcp \
  -H 'content-type: application/json' \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"search_comparisons","arguments":{"query":"password manager","lang":"en","country":"US","limit":5}}}'

Other machine formats

Every comparison is also available without MCP, at the same URL plus a suffix:

  • …/{lang}/{country}/{slug}.json - structured data (scores, sources, freshness)
  • …/{lang}/{country}/{slug}.md - clean Markdown
  • Discovery guide for agents: https://comparefairly.com/llms.txt

License & data terms

This repository (code and docs) is provided under the MIT License. The comparison data returned by the server is subject to CompareFairly's usage terms: citation with a link is welcome; bulk extraction and redistribution of the catalog is not permitted. For volume access, use the contact page.

Links

  • Website: https://comparefairly.com
  • MCP endpoint: https://comparefairly.com/mcp
  • Agent guide: https://comparefairly.com/llms.txt

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured