How to Use the Pinecone MCP in Google ADK

Q: Can I restrict which Pinecone tools my Google ADK agent uses?

Absolutely. Use the toolnames filter to whitelist only the operations you want your agent to perform, such as read-only queries.

Q: Does this support long-context agents?

Yes. By using fetchvectors, you can pull specific context into your agent's massive 1M+ token window without performing redundant searches.

Connect your Google ADK agents to Pinecone to search and manage vector databases with enterprise-grade reliability.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Pinecone MCP to Google ADK

Create your Vinkius account to connect Pinecone to Google ADK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Pinecone with Google ADK

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Perplexity

Efficient similarity searches

Your agent performs vector lookups using `query_vectors` to find matches. This integration works natively with your existing Google ADK workflow. It returns top-k results with metadata, allowing your Gemini-powered agent to reason over the retrieved data immediately.

Index configuration management

Query the state of your infrastructure using `describe_index`. It gives your agent the dimensions and metric type of any index it touches. This prevents configuration mismatches, ensuring your Google ADK agent always sends vectors that match your index specs.

Collection and index listing

Map your entire database structure by calling `list_collections`. Your agent uses this to navigate complex multi-tenant environments dynamically. It automates the discovery process, so you don't have to hardcode index names into your Google ADK agent configuration.

Setup guide

Set up Pinecone MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Pinecone tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Pinecone_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Pinecone tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Pinecone MCP in Google ADK

You instantiate the MCP toolset and pass it to your agent. From there, `query_vectors` becomes a standard tool available to your LLM.

Yes. The server supports Stdio and HTTP transports, making it compatible with any Google ADK agent running in a cloud environment.

Absolutely. Use the tool_names filter to whitelist only the operations you want your agent to perform, such as read-only queries.

Yes. By using `fetch_vectors`, you can pull specific context into your agent's massive 1M+ token window without performing redundant searches.

The server operates in an ephemeral sandbox. It only processes the vector metadata you explicitly request, and all connections are terminated after the task completes.

Use it with your favorite AI tools

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