Atlaso Labs MCP vs Perenna

A side-by-side look at the Atlaso Labs MCP and Perenna MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.

Atlaso Labs MCP logoAtlaso Labs MCPKnowledge & Memory · Remote HTTP/SSEAI memory layer — one shared, persistent memory across every AI tool you connect.37FairAuth: not declaredPerenna logoPerennaKnowledge & Memory · Local subprocess (stdio)A lightweight, Git-backed permanent memory for AI agents.41FairAuth: not declared

Verdict: choose Atlaso Labs MCP for knowledge & memory work over a hosted endpoint; choose Perenna for knowledge & memory as a local process. You can also run both side by side in one mcpServers block.

Which MCP server should you choose?

Atlaso Labs MCP logoChoose Atlaso Labs MCP when

  • You need dedicated capabilities in the Knowledge & Memory domain.
  • You prefer a remote HTTP/SSE transport.
Explore Atlaso Labs MCP

Perenna logoChoose Perenna when

  • You need dedicated capabilities in the Knowledge & Memory domain.
  • You prefer a local stdio subprocess transport.
  • You have the keys it needs: VEXOR_CONFIG_JSON, VEXOR_API_KEY.
Explore Perenna

Feature & specification comparison

Rows where the two servers differ are marked.

SpecAtlaso Labs MCP logoAtlaso Labs MCPPerenna logoPerenna
CategoryKnowledge & MemoryKnowledge & Memory
Quality signal37/100 · Fair41/100 · Fair
Install commandConfirmedConfirmed
TransportRemote HTTP/SSELocal subprocess (stdio)
AuthNot declaredNot declared
PricingNot declaredNot declared
Required env varsNone listedVEXOR_CONFIG_JSONVEXOR_API_KEY
Verified ownerCommunity listingCommunity listing
GitHub stars061
Directory activity 0 views 0 installs 0 upvotes 2 views 0 installs 0 upvotes
ToolsNot listedNot listed

Tools & capabilities

Atlaso Labs MCP tools

No tool names declared yet. Check the project README from the listing page.

Perenna tools

No tool names declared yet. Check the project README from the listing page.

Ready-to-paste client configs

Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.

Atlaso Labs MCP

mcpServers (Claude Desktop / Cursor)
{
  "mcpServers": {
    "mcp-3": {
      "url": "https://mcp.atlaso.ai/mcp"
    }
  }
}

Perenna

mcpServers (Claude Desktop / Cursor)
{
  "mcpServers": {
    "perenna": {
      "command": "uvx",
      "args": [
        "vexor"
      ],
      "env": {
        "VEXOR_CONFIG_JSON": "YOUR_VEXOR_CONFIG_JSON_HERE",
        "VEXOR_API_KEY": "YOUR_VEXOR_API_KEY_HERE"
      }
    }
  }
}

Frequently asked questions

Atlaso Labs MCP is categorized under Knowledge & Memory and uses a remote HTTP/SSE transport. Perenna belongs to Knowledge & Memory and uses a local stdio subprocess. Pick Atlaso Labs MCP for knowledge & memory work and Perenna for knowledge & memory.

Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.

Atlaso Labs MCP has no declared pricing — check the project's own site. Perenna has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.

Yes. MCP clients support multiple servers: add mcp-3 and perenna as separate keys in the same mcpServers object, and your assistant will call tools from either one.

On AllMCPs, Atlaso Labs MCP has a quality signal of 37/100 (Fair) with 0 views, 0 installs and 0 GitHub stars. Perenna has 41/100 (Fair) with 2 views, 0 installs and 61 GitHub stars.