Can the AI execute raw vector similarity searches?

Yes, absolutely. Once you supply the raw semantic embedding coordinates (normally a float array generated previously), the LLM can funnel it through the `query_vectors` tool. The Pinecone DB will process this and return the top-K closest vector matches along with embedded metadata.

How do I check my remaining vector storage capacity?

It's extremely simple. Just ask the connected AI agent to 'Get the index stats'. It will internally call `get_index_stats` against the specified index namespace, returning total vector count and physical dimensionality limits to your chat window.

Is it safe to delete vectors dynamically using the chat terminal?

Yes, but with standard precautions. The `delete_vectors` tool operates exactly as the official SDK. As long as you maintain clear contextual scopes and ID filtering in your prompts, the execution is purely deterministic and secure.

Pinecone MCP Server for Google ADK 7 tools — connect in under 2 minutes

Name: Pinecone
Availability: InStock
Author: Vinkius

google.github.io/adk-docs/tools/mcp-tools

Built by Vinkius GDPR 7 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Pinecone as an MCP tool provider through the Vinkius and your ADK agents can call every tool with full schema introspection.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token — get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="pinecone_agent",
    instruction=(
        "You help users interact with Pinecone "
        "using 7 available tools."
    ),
    tools=[mcp_tools],
)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Pinecone MCP Server

Connect your Pinecone knowledge graph environment straight into your AI agent's logic. Give your preferred Large Language Model the keys to fetch, query, and modify vector spaces via natural language context without leaving the chat interface.

Google ADK natively supports Pinecone as an MCP tool provider — declare the Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 7 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

What you can do

Index Hierarchy — Retrieve structural blueprints instantly using list_indexes and fetch intricate topology parameters utilizing describe_index.
Semantic Harvesting — Pass pure array values to execute blazing-fast retrieval with query_vectors, or pinpoint specific embeddings natively employing fetch_vectors.
Space Archiving — Monitor grouped snapshot arrays leveraging list_collections and perform surgical cleanups executing delete_vectors accurately.
Performance Auditing — Ask the model to pull real-time health checks calling get_index_stats to reveal vector capacity limits across pods.

The Pinecone MCP Server exposes 7 tools through the Vinkius. Connect it to Google ADK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Pinecone to Google ADK via MCP

Follow these steps to integrate the Pinecone MCP Server with Google ADK.

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 7 tools from Pinecone via MCP

Why Use Google ADK with the Pinecone MCP Server

Google ADK provides unique advantages when paired with Pinecone through the Model Context Protocol.

Google ADK natively supports MCP tool servers — declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Pinecone

Production-ready features like session management, evaluation, and deployment come built-in — not bolted on

Seamless integration with Google Cloud services means you can combine Pinecone tools with BigQuery, Vertex AI, and Cloud Functions

Pinecone + Google ADK Use Cases

Practical scenarios where Google ADK combined with the Pinecone MCP Server delivers measurable value.

Enterprise data agents: ADK agents query Pinecone and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine Pinecone tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query Pinecone regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Pinecone

Pinecone MCP Tools for Google ADK (7)

These 7 tools become available when you connect Pinecone to Google ADK via MCP:

delete_vectors

Delete vectors from an index

describe_index

Get configuration details for an index

fetch_vectors

Fetch specific vectors by their IDs

get_index_stats

Get usage statistics for an index

list_collections

List all index collections

list_indexes

List all Pinecone indexes

query_vectors

Returns the most similar vectors and their metadata. Search for similar vectors

Example Prompts for Pinecone in Google ADK

Ready-to-use prompts you can give your Google ADK agent to start working with Pinecone immediately.

"Check the vector count stats for the index named `document-embeddings`."

"Delete all vectors belonging to the user ID 'auth-abc123' namespace."

"List all existing collections created in my Pinecone environment."

Troubleshooting Pinecone MCP Server with Google ADK

Common issues when connecting Pinecone to Google ADK through the Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

Pinecone + Google ADK FAQ

Common questions about integrating Pinecone MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.