MongoDB Atlas Vector Search MCP Server for Google ADK 6 tools — connect in under 2 minutes
Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add MongoDB Atlas Vector Search as an MCP tool provider through the Vinkius and your ADK agents can call every tool with full schema introspection.
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Vinkius supports streamable HTTP and SSE.
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="mongodb_atlas_vector_search_agent",
instruction=(
"You help users interact with MongoDB Atlas Vector Search "
"using 6 available tools."
),
tools=[mcp_tools],
)
* 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 MongoDB Atlas Vector Search MCP Server
Connect your MongoDB Atlas cluster to any AI agent and take full control of your high-performance vector search, embedding storage, and operational data management through natural conversation.
Google ADK natively supports MongoDB Atlas Vector Search as an MCP tool provider — declare the Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 6 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
- Vector Similarity Search — Execute sophisticated '$vectorSearch' queries against your collections to retrieve semantically relevant matches using raw embedding vectors directly from your agent
- Unified Data Management — Find, insert, and delete standard MongoDB documents using literal MQL (MongoDB Query Language) filters to manage both vector and operational data in a single system
- Search Index Provisioning — Create and configure Atlas Search indices with custom dimensions and mapping definitions to optimize your cluster's similarity calculation infrastructure
- Collection Lifecycle Audit — List all managed data collections and retrieve schema boundaries to understand namespace references and database organization natively
- Real-time Ingestion — Synchronize new JSON records into your collections, allowing for instant searchability and automated vector parsing if Atlas triggers are enabled
- Precision Retrieval — Execute targeted MQL queries to fetch specific data points or metadata chunks, bypassing vector logic for rapid structural verification and auditing
The MongoDB Atlas Vector Search MCP Server exposes 6 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 MongoDB Atlas Vector Search to Google ADK via MCP
Follow these steps to integrate the MongoDB Atlas Vector Search 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 6 tools from MongoDB Atlas Vector Search via MCP
Why Use Google ADK with the MongoDB Atlas Vector Search MCP Server
Google ADK provides unique advantages when paired with MongoDB Atlas Vector Search 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 MongoDB Atlas Vector Search
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 MongoDB Atlas Vector Search tools with BigQuery, Vertex AI, and Cloud Functions
MongoDB Atlas Vector Search + Google ADK Use Cases
Practical scenarios where Google ADK combined with the MongoDB Atlas Vector Search MCP Server delivers measurable value.
Enterprise data agents: ADK agents query MongoDB Atlas Vector Search and cross-reference results with internal databases for comprehensive analysis
Multi-modal workflows: combine MongoDB Atlas Vector Search tool responses with Gemini's vision and language capabilities in a single agent
Automated compliance checks: schedule ADK agents to query MongoDB Atlas Vector Search regularly and flag policy violations or configuration drift
Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including MongoDB Atlas Vector Search
MongoDB Atlas Vector Search MCP Tools for Google ADK (6)
These 6 tools become available when you connect MongoDB Atlas Vector Search to Google ADK via MCP:
create_index
Create literal standard embedding Search Index bound to dimensions
delete
Delete literal documents bounded by the parsed MongoDB filters
find
Find standard MongoDB documents resolving standard query filters
insert
Insert a distinct generic document into standard target collection
list_collections
List accessible data collections bound explicitly inside Atlas limits
search
Perform highly-dimensional Vector similarity search using $vectorSearch
Example Prompts for MongoDB Atlas Vector Search in Google ADK
Ready-to-use prompts you can give your Google ADK agent to start working with MongoDB Atlas Vector Search immediately.
"Vector search in 'knowledge_base' for vector: [0.1, -0.2, ...]"
"Find active users in the 'users' collection with plan 'pro'"
"List all collections in the 'production' database"
Troubleshooting MongoDB Atlas Vector Search MCP Server with Google ADK
Common issues when connecting MongoDB Atlas Vector Search to Google ADK through the Vinkius, and how to resolve them.
McpToolset not found
pip install --upgrade google-adkMongoDB Atlas Vector Search + Google ADK FAQ
Common questions about integrating MongoDB Atlas Vector Search MCP Server with Google ADK.
How does Google ADK connect to MCP servers?
Can ADK agents use multiple MCP servers?
Which Gemini models work best with MCP tools?
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Connect MongoDB Atlas Vector Search to Google ADK
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.