Can I test alternative AI models entirely within the terminal using the Glama integration?

Yes. Tools like `glama_get_gateway_models` list available OpenAI-compatible proxies, and `glama_run_gateway_chat` allows your Vinkius agent to run text completions outside itself natively.

Does the Glama server provide telemetry data back to the registry?

Yes. Active MCP usage events can be logged seamlessly applying the `glama_send_telemetry` tool in specific sequences to inform publishers about proxy executions.

Are private hosted instances queryable?

Yes. By executing `glama_get_hosted_instances`, your agent limits queries exclusively to private proxies explicitly belonging to your linked environment.

Glama MCP Server for Google ADK 8 tools — connect in under 2 minutes

Name: Glama
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 8 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Glama as an MCP tool provider through 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="glama_agent",
    instruction=(
        "You help users interact with Glama "
        "using 8 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 Glama MCP Server

Empower your local Vinkius terminal intelligence with the Glama.ai infrastructure bridge. Rather than navigating generic web interfaces to find compatible model contexts, let your core logic intuitively search, index, and introspect external MCP servers on the fly. In addition, harness the power to query multiple standard LLM networks via the Glama API Gateway, consolidating all programmatic text completion requirements cleanly.

Google ADK natively supports Glama as an MCP tool provider. declare Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 8 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

MCP Registry Scuba — Seamlessly query list_mcp_servers and get_mcp_server_info to find context protocols needed dynamically without interrupting deep-work focus states.
Gateway Proxies — List active LLM models navigating list_gateway_models and push semantic prompts via run_gateway_chat executing parallel logic chains outside local memory.
Matrix Attributes — Uncover standard classification strings with get_mcp_attributes assessing global MCP logic matrices.
Hosted Telemetry — Scan local instances routing get_hosted_instances and actively parse behavior metrics pushing logs through send_telemetry.

The Glama MCP Server exposes 8 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 Glama to Google ADK via MCP

Follow these steps to integrate the Glama 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 8 tools from Glama via MCP

Why Use Google ADK with the Glama MCP Server

Google ADK provides unique advantages when paired with Glama 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 Glama

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 Glama tools with BigQuery, Vertex AI, and Cloud Functions

Glama + Google ADK Use Cases

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

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

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

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

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

Glama MCP Tools for Google ADK (8)

These 8 tools become available when you connect Glama to Google ADK via MCP:

glama_get_gateway_model_details

g. "anthropic/claude-3-5-sonnet") to fetch the specific configurations exposed by the Glama unified API proxy. Investigate granular attributes (prices, context window, parameters) of a specific proxied Gateway Model

glama_get_gateway_models

Audit the complete list of AI models supported natively by the Glama OpenAI-compatible gateway

glama_get_hosted_instances

Cannot access public instances natively from here. Fetch all Private Hosted MCP instances assigned to your specific Glama account

glama_get_mcp_attributes

List filtering attributes and semantic categorizations mapped within the Glama MCP Registry

glama_get_mcp_server_info

Requires its namespace and slug. Extract detailed parameters and installation instructions for a specific Glama MCP server

glama_list_mcp_servers

Capable of loose text matching to discover new agentic capabilities. Search and list MCP servers directly from the global Glama directory

glama_run_gateway_chat

Bifurcate an isolated conversational prompt using a specific model through the Glama proxy network

glama_send_telemetry

Can be triggered after your AI uses a specific external server. Report semantic usage execution metrics back to the Glama Telemetry backend

Example Prompts for Glama in Google ADK

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

"Find all MCP servers relating to CRM logic inside the registry, then let me know their basic descriptions."

"Are there smaller LLMs available on the Glama API gateway we can proxy text to quickly?"

"Report a successful telemetry execution map event back to Glama for the GitHub repo tool."

Troubleshooting Glama MCP Server with Google ADK

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

McpToolset not found

Update: pip install --upgrade google-adk

Glama + Google ADK FAQ

Common questions about integrating Glama 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.