Glama MCP Server for AutoGen 8 tools — connect in under 2 minutes
Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Glama as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with McpWorkbench(
server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
transport="streamable_http",
) as workbench:
tools = await workbench.list_tools()
agent = AssistantAgent(
name="glama_agent",
tools=tools,
system_message=(
"You help users with Glama. "
"8 tools available."
),
)
print(f"Agent ready with {len(tools)} tools")
asyncio.run(main())
* 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.
AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Glama tools. Connect 8 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.
What you can do
- MCP Registry Scuba — Seamlessly query
list_mcp_serversandget_mcp_server_infoto find context protocols needed dynamically without interrupting deep-work focus states. - Gateway Proxies — List active LLM models navigating
list_gateway_modelsand push semantic prompts viarun_gateway_chatexecuting parallel logic chains outside local memory. - Matrix Attributes — Uncover standard classification strings with
get_mcp_attributesassessing global MCP logic matrices. - Hosted Telemetry — Scan local instances routing
get_hosted_instancesand actively parse behavior metrics pushing logs throughsend_telemetry.
The Glama MCP Server exposes 8 tools through the Vinkius. Connect it to AutoGen 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 AutoGen via MCP
Follow these steps to integrate the Glama MCP Server with AutoGen.
Install AutoGen
Run pip install "autogen-ext[mcp]"
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Integrate into workflow
Use the agent in your AutoGen multi-agent orchestration
Explore tools
The workbench discovers 8 tools from Glama automatically
Why Use AutoGen with the Glama MCP Server
AutoGen provides unique advantages when paired with Glama through the Model Context Protocol.
Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Glama tools to solve complex tasks
Role-based architecture lets you assign Glama tool access to specific agents. a data analyst queries while a reviewer validates
Human-in-the-loop support: agents can pause for human approval before executing sensitive Glama tool calls
Code execution sandbox: AutoGen agents can write and run code that processes Glama tool responses in an isolated environment
Glama + AutoGen Use Cases
Practical scenarios where AutoGen combined with the Glama MCP Server delivers measurable value.
Collaborative analysis: one agent queries Glama while another validates results and a third generates the final report
Automated review pipelines: a researcher agent fetches data from Glama, a critic agent evaluates quality, and a writer produces the output
Interactive planning: agents negotiate task allocation using Glama data to make informed decisions about resource distribution
Code generation with live data: an AutoGen coder agent writes scripts that process Glama responses in a sandboxed execution environment
Glama MCP Tools for AutoGen (8)
These 8 tools become available when you connect Glama to AutoGen 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 AutoGen
Ready-to-use prompts you can give your AutoGen 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 AutoGen
Common issues when connecting Glama to AutoGen through the Vinkius, and how to resolve them.
McpWorkbench not found
pip install "autogen-ext[mcp]"Glama + AutoGen FAQ
Common questions about integrating Glama MCP Server with AutoGen.
How does AutoGen connect to MCP servers?
Can different agents have different MCP tool access?
Does AutoGen support human approval for tool calls?
Connect Glama with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Glama to AutoGen
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.