What is the key difference between Ai·rete·rag and RAGScore?

Ai·rete·rag belongs to the Knowledge & Memory category with local stdio execution, while RAGScore belongs to the Knowledge & Memory category with local stdio execution.

How do I install Ai·rete·rag or RAGScore in Claude Desktop or Cursor?

Copy the mcpServers JSON configuration snippet provided on this page into your claude_desktop_config.json or ~/.cursor/mcp.json file and restart your AI client.

Are Ai·rete·rag and RAGScore free or paid?

Ai·rete·rag operates under a Free / Open Source model (No auth required). RAGScore operates under a Free / Open Source model (No auth required).

Which MCP server has more tools and community activity?

Ai·rete·rag lists 0 tools with 0 views and 0 GitHub stars. RAGScore lists 0 tools with 0 views and 15 GitHub stars.

Can I use both Ai·rete·rag and RAGScore together in the same MCP host?

Yes! MCP hosts like Claude Desktop, Cursor, Windsurf, Cline, and VS Code support configuring multiple servers under the mcpServers object simultaneously.

How do I install Ai·rete·rag or RAGScore in Claude Desktop, Cursor, or Windsurf?

Both servers follow the standard Model Context Protocol configuration format. Simply copy the JSON block from the configuration section above and paste it into your AI client's mcpServers configuration object (for instance in claude_desktop_config.json or ~/.cursor/mcp.json ), then completely restart or refresh the application.

Are Ai·rete·rag and RAGScore free to use, or do they require API keys?

Ai·rete·rag is listed under a Free / Open Source model with No auth required . RAGScore operates under a Free / Open Source model with No auth required . If environment variables are required (such as API keys), be sure to define them under the env key in your MCP client's configuration file.

Can I run both Ai·rete·rag and RAGScore at the same time in my AI client?

Yes! MCP clients support multi-server orchestration. You can include both ai-rete-rag and ragscore as distinct keys inside the single mcpServers object in your configuration file. Your AI assistant will automatically select and call the appropriate tool from either server during chat sessions.

Which MCP server has higher directory engagement and quality ratings?

On AllMCPs, Ai·rete·rag has a Quality Score of 20 /100 ( Emerging ) with 0 views, 0 installs, and 0 GitHub stars. RAGScore holds a Quality Score of 41 /100 ( Fair ) with 0 views, 0 installs, and 15 GitHub stars.

Side-by-Side Model Context Protocol Comparison

Ai·rete·rag vs RAGScore

In-depth architectural comparison of the Ai·rete·rag and RAGScore MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.

At a Glance & Executive Verdict

Ai·rete·rag

Knowledge & Memory · Local stdio

Quality: 20/100 (Emerging) | Auth: No auth required

RAGScore

Knowledge & Memory · Local stdio

Quality: 41/100 (Fair) | Auth: No auth required

Verdict Summary: Choose Ai·rete·rag if you need specialized Knowledge & Memory tools running via a local process. Choose RAGScore if your workspace requires Knowledge & Memory integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.

Which MCP Server Should You Choose?

Choose Ai·rete·rag when:

You need dedicated capabilities in the Knowledge & Memory domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).

Explore Ai·rete·rag Details

Choose RAGScore when:

You need dedicated capabilities in the Knowledge & Memory domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).

Explore RAGScore Details

Feature & Specification Comparison

Specification	A Ai·rete·rag Knowledge & Memory	RAGScore Knowledge & Memory
Summary	Auditable rule-based decisions with RAG explanations: a Rete engine decides, an LLM explains why.	Generate QA datasets & evaluate RAG systems. Privacy-first, any LLM, local or cloud.
Category & Scope	Knowledge & Memory	Knowledge & Memory
Quality signal

Tools & Capabilities Breakdown

Ai·rete·rag Tools (0)

No explicit tool names declared in metadata yet. Check project README on main listing page.

RAGScore Tools (0)

No explicit tool names declared in metadata yet. Check project README on main listing page.

Ready-to-Paste Client Configurations

Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).

Ai·rete·rag Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "ai-rete-rag": {
      "command": "npx",
      "args": [
        "-y",
        "ai-rete-rag"
      ]
    }
  }
}

RAGScore Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "ragscore": {
      "command": "uvx",
      "args": [
        "ragscore"
      ]
    }
  }
}

Frequently Asked Questions

Ai·rete·rag is categorized under Knowledge & Memory and uses a local stdio subprocess. In contrast, RAGScore belongs to Knowledge & Memory using local stdio subprocess. Select Ai·rete·rag when you need capabilities focused on knowledge & memory and RAGScore when you require tools for knowledge & memory.

More alternatives to Ai·rete·rag More alternatives to RAGScore Knowledge & Memory category hub