Can the agent perform vector plus text-filtering search combined natively?

Yes. Provide the agent with the collection name alongside the text payload and tell it the exact vector structure. It leverages internal filters querying natively and returns the nearest neighbors with exact accuracy scores.

How do I make the AI create a semantic collection ready for embeddings (OpenAI 1536 dims)?

Ask the agent to use 'create_collection'. Provide standard JSON declaring the name, the field structure, and explicitly define the `float[]` field tracking the 1536 dims length. The cluster will spin the framework up instantly.

Can it delete problematic vectors holding bad geometry data manually?

Absolutely. Supplying the explicit collection target and the item 'id' to the delete_document prompt securely wipes out all traces from the dataset. Use this sparingly as it can't be undone easily.

Typesense Vector Search MCP Server for Google ADK 6 tools — connect in under 2 minutes

Name: Typesense Vector Search
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 6 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Typesense 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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ASK AI ABOUT THIS MCP SERVER

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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="typesense_vector_search_agent",
    instruction=(
        "You help users interact with Typesense Vector Search "
        "using 6 available tools."
    ),
    tools=[mcp_tools],
)

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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 Typesense Vector Search MCP Server

Connect your Typesense Vector Search environment to any AI agent and take full autonomous control over vector collections, indexing processes, and semantic querying through daily conversation.

Google ADK natively supports Typesense 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 Semantic Search — Issue combined text-filtering alongside vector similarity (vec) queries natively through chat
Collection Provisioning — Instantly create new semantic schema datasets holding complex vector embedding structures organically
Document Indexing — Let your AI insert or update JSON payloads into your database, bypassing manual code-level REST integrations
Schema & Records Insights — Retrieve absolute schema geometries mapping collections to ensure developers map fields correctly

The Typesense 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 Typesense Vector Search to Google ADK via MCP

Follow these steps to integrate the Typesense 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 Typesense Vector Search via MCP

Why Use Google ADK with the Typesense Vector Search MCP Server

Google ADK provides unique advantages when paired with Typesense 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 Typesense 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 Typesense Vector Search tools with BigQuery, Vertex AI, and Cloud Functions

Typesense Vector Search + Google ADK Use Cases

Practical scenarios where Google ADK combined with the Typesense Vector Search MCP Server delivers measurable value.

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

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

Automated compliance checks: schedule ADK agents to query Typesense 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 Typesense Vector Search

Typesense Vector Search MCP Tools for Google ADK (6)

These 6 tools become available when you connect Typesense Vector Search to Google ADK via MCP:

create_collection

Provide the schema details as a JSON object. Creates a new search collection with a specific schema

delete_document

This action is irreversible. Permanently removes a document from a collection by its ID

get_collection_details

Retrieves schema and metadata for a specific collection

index_document

Provide the collection name and the document data as a JSON object. Adds or updates a document in a search collection

list_vector_collections

Lists all collections in the Typesense instance

search_vectors

Provide the collection name, a text query, and a vector_query string (e.g., "vec:(0.1, 0.2, ...)"). Performs a vector similarity search combined with optional text filtering

Example Prompts for Typesense Vector Search in Google ADK

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

"List all active collections on this vector cluster. Do I have any collections initialized yet?"

"I have an embedding snippet: [0.34, 0.42, 0.99...]. Delete the document carrying ID 'test-123' and re-index it using this JSON data on collection 'faqs'."

"Explain the schema definitions used inside the 'products_inventory' collection."

Troubleshooting Typesense Vector Search MCP Server with Google ADK

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

McpToolset not found

Update: pip install --upgrade google-adk

Typesense Vector Search + Google ADK FAQ

Common questions about integrating Typesense Vector Search 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.