What is the key difference between Rag Knowledge Mcp and Cortex?

Rag Knowledge Mcp belongs to the Knowledge & Memory category with local stdio execution, while Cortex belongs to the Knowledge & Memory category with cloud HTTP/SSE execution.

How do I install Rag Knowledge Mcp or Cortex 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 Rag Knowledge Mcp and Cortex free or paid?

Rag Knowledge Mcp operates under a Free / Open Source model (No auth required). Cortex operates under a Free / Open Source model (No auth required).

Which MCP server has more tools and community activity?

Rag Knowledge Mcp lists 0 tools with 0 views and 0 GitHub stars. Cortex lists 0 tools with 0 views and 0 GitHub stars.

Can I use both Rag Knowledge Mcp and Cortex 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 Rag Knowledge Mcp or Cortex 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 Rag Knowledge Mcp and Cortex free to use, or do they require API keys?

Rag Knowledge Mcp is listed under a Free / Open Source model with No auth required . Cortex 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 Rag Knowledge Mcp and Cortex at the same time in my AI client?

Yes! MCP clients support multi-server orchestration. You can include both rag-knowledge-mcp and cortex-2 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, Rag Knowledge Mcp has a Quality Score of 37 /100 ( Fair ) with 0 views, 0 installs, and 0 GitHub stars. Cortex holds a Quality Score of 45 /100 ( Fair ) with 0 views, 0 installs, and 0 GitHub stars.

Side-by-Side Model Context Protocol Comparison

Rag Knowledge Mcp vs Cortex

In-depth architectural comparison of the Rag Knowledge Mcp and Cortex 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

Rag Knowledge Mcp

Knowledge & Memory · Local stdio

Quality: 37/100 (Fair) | Auth: No auth required

Cortex

Knowledge & Memory · Remote HTTP/SSE

Quality: 45/100 (Fair) | Auth: No auth required

Verdict Summary: Choose Rag Knowledge Mcp if you need specialized Knowledge & Memory tools running via a local process. Choose Cortex if your workspace requires Knowledge & Memory integration with remote web transport. Both servers can be configured concurrently in your client's mcpServers manifest.

Which MCP Server Should You Choose?

Choose Rag Knowledge Mcp 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 Rag Knowledge Mcp Details

Choose Cortex when:

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

Explore Cortex Details

Feature & Specification Comparison

Specification	R Rag Knowledge Mcp Knowledge & Memory	C Cortex Knowledge & Memory
Summary	MCP server for rag knowledge. Features semantic search, knowledge graph query, index documen...	Shared, persistent semantic memory for your AI: save, search by meaning, recall with sources.
Category & Scope	Knowledge & Memory	Knowledge & Memory
Quality signal

Tools & Capabilities Breakdown

Rag Knowledge Mcp Tools (0)

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

Cortex 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).

Rag Knowledge Mcp Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "rag-knowledge-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli@latest"
      ]
    }
  }
}

Cortex Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "cortex-2": {
      "url": "https://skynetlab-cortex.com"
    }
  }
}

Frequently Asked Questions

Rag Knowledge Mcp is categorized under Knowledge & Memory and uses a local stdio subprocess. In contrast, Cortex belongs to Knowledge & Memory using remote streaming HTTP/SSE transport. Select Rag Knowledge Mcp when you need capabilities focused on knowledge & memory and Cortex when you require tools for knowledge & memory.

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