Follow these steps to run the MCP Pinecone tool and chatbot inside a Meshagent Room in the cloud for quick testing. When the room closes (e.g., due to inactivity), the MCP server and chatbot are removed automatically.

Environment Variables

  • PINECONE_ASSISTANT_HOST: Your Pinecone Assistant host.
  • PINECONE_API_KEY: Your Pinecone API key.

Steps

  1. Install Meshagent:
    pip install "meshagent[all]"
    
  2. Sign Up & Authenticate: Follow the Meshagent CLI getting started guide.
  3. Start Pinecone MCP Service in a Room:
    meshagent service test \
      --room=test \
      --role=agent \
      --image=meshagent/mcp-pinecone:latest \
      --env MESHAGENT_PORT=8001 \
      --env PINECONE_ASSISTANT_HOST=<YOUR_PINECONE_ASSISTANT_HOST_HERE> \
      --env PINECONE_API_KEY=<YOUR_PINECONE_API_KEY_HERE> \
      --port="num=8001 path=/webhook liveness=/ type=meshagent.callable" \
      --name=mcp-pinecone-service-test
    
    This command will start a Meshagent Room named “test”, running the Pinecone MCP server. The room and its services will close if inactive.
  4. Join a Chatbot with the Toolkit in the Room:
    meshagent chatbot join \
      --room=test \
      --agent-name=mcp-pinecone \
      --name=mcp-pinecone \
      --toolkit=mcp-pinecone
    
    • The chatbot will be able to use Pinecone tools in the room.
    • Room link will be printed in the output for browser-based chat.
  5. Test the Integration:
    • Open the room link from the chatbot join command.
    • Send a message to the agent to interact live with the MCP Server tools.

Project Level Deployment

To make the MCP server tools and a chatbot persistently available in every room of a Meshagent project (ideal for production), deploy as project-level services:
  1. Create Persistent MCP Pinecone Service:
    meshagent service create \
      --role=agent \
      --image=meshagent/mcp-pinecone:latest \
      --env MESHAGENT_PORT=8001 \
      --env PINECONE_ASSISTANT_HOST=<YOUR_PINECONE_ASSISTANT_HOST_HERE> \
      --env PINECONE_API_KEY=<YOUR_PINECONE_API_KEY_HERE> \
      --port="num=8001 path=/webhook liveness=/ type=meshagent.callable" \
      --name=mcp-pinecone-service
    
  2. Create Persistent Chatbot Service with Toolkit:
    meshagent service create \
      --image="meshagent/cli:latest" \
      --port="num=9001 path=/agent liveness=/ type=meshagent.callable participant_name=mcp-pinecone-chatbot" \
      --env="MESHAGENT_PORT=9001" \
      --name="mcp-pinecone-chatbot-service" \
      --command="meshagent chatbot service --agent-name=mcp-pinecone-chatbot --toolkit=mcp-pinecone"
    
These services will join every new room created within your Meshagent project, so your tools and chatbot are always available—no need to run them manually!

Tools Available

assistant_context

Retrieves relevant document snippets from your Pinecone Assistant knowledge base. Short Description:
assistant_context | Retrieves relevant document snippets from your Pinecone Assistant knowledge base.

Tool Details

assistant_context
Retrieves relevant document snippets from your Pinecone Assistant knowledge base.
Returns an array of text snippets from the most relevant documents.
  • assistant_name (string): Name of an existing Pinecone assistant
  • query (string): The query to retrieve context for.
  • top_k (integer, optional): Number of context snippets to retrieve. Defaults to 15. Recommended: 5-8 for simple/narrow queries, 10-20 for complex/broad topics.

Meshagent & MCP Resources


For more details about MCP servers and Pinecone’s Assistant integration, see Pinecone Assistant MCP Server Repository and this Docker image.