TampifyAI MCP Server

Exposes the TampifyAI knowledge system to any MCP client (Claude Desktop, and anything else that speaks the Model Context Protocol) as callable tools.

A thin adapter: it wraps the Librarian (retrieval + answer) and Scout (prospect screening) that already exist, so each behaviour has one implementation, not two.

Tools exposed

Related MCP server: rbac-rag-assistant

Why MCP

Instead of every agent re-implementing "how do I ask the business a question", the knowledge system is exposed once as MCP tools. Any client - Claude Desktop today, something else tomorrow - can call them. The business knowledge stays in one place and the access layer is standardised.

Verified

All four tools were called end-to-end through the server's own handler:

ask_brain        -> retrieved from the brain
get_pricing      -> returned the pricing table
screen_prospects -> OK Tampa Pole Fitness; SKIP Gro Yoga - on the blacklist

screen_prospects correctly qualified one candidate and rejected another as blacklisted - Scout's rules running through the MCP layer.

Setup

pip install -r requirements.txt
# also needs the Librarian's deps (chromadb, sentence-transformers) - see its repo

Run

python server.py            # stdio transport, for MCP clients

Wire it into Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "tampifyai": {
      "command": "python",
      "args": ["/absolute/path/to/tampify-librarian-mcp/server.py"],
      "env": {
        "BRAIN_REPO_PATH": "/absolute/path/to/tampify-second-brain"
      }
    }
  }
}

Restart Claude Desktop; the tools appear under the MCP connector.

Stack

Python · MCP Python SDK (mcp[cli], v2 - MCPServer)

Status

Working: all four tools verified end-to-end. Next: a get_leak tool and streaming the Librarian's trace metadata back to the client.