MCP and Function Calling

Function calling lets your AI agent execute Python functions and access external services during conversations.

Vision Agents uses Stream Video for real-time WebRTC transport by default. External WebRTC transports are supported as well. Most AI providers offer free tiers to get started.

For a conceptual overview of MCP, see Model Context Protocol.

Registering Functions

Use @llm.register_function() to make any Python function callable by the LLM:

from vision_agents.plugins import openai

llm = openai.LLM(model="gpt-5.4")

@llm.register_function(description="Get current weather for a location")
async def get_weather(location: str) -> dict:
    return {"location": location, "temperature": "22°C", "condition": "Sunny"}

@llm.register_function(description="Calculate the sum of two numbers")
async def calculate_sum(a: int, b: int) -> int:
    return a + b

Only async functions can be registered. Passing a synchronous function to @register_function() raises a ValueError.

The LLM automatically calls these functions when relevant:

response = await llm.simple_response("What's the weather in London?")
# Calls get_weather("London") and incorporates result

Parameters and Types

Functions support required and optional parameters:

@llm.register_function(description="Search for products")
async def search_products(
    query: str,                    # Required
    category: str = "all",         # Optional with default
    max_price: float = 1000.0,
    in_stock: bool = True
) -> list:
    return [{"name": "Product 1", "price": 29.99}]

Custom Function Names

Override the function name exposed to the LLM:

@llm.register_function(
    name="check_permissions",
    description="Check if a user has specific permissions"
)
async def verify_user_access(user_id: str, permission: str) -> bool:
    return True

MCP Servers

MCP servers provide your agent with access to external tools and services.

Local Servers

Run on your machine via stdio:

from vision_agents.core.mcp import MCPServerLocal

local_server = MCPServerLocal(
    command="uv run my_mcp_server.py",
    session_timeout=300.0
)

Remote Servers

Connect over HTTP:

from vision_agents.core.mcp import MCPServerRemote

github_server = MCPServerRemote(
    url="https://api.githubcopilot.com/mcp/",
    headers={"Authorization": f"Bearer {token}"},
    timeout=10.0,
    session_timeout=300.0
)

Connecting to Agent

Pass MCP servers to your agent — tools are automatically discovered and registered:

from vision_agents.core import Agent, User
from vision_agents.plugins import openai, getstream

agent = Agent(
    edge=getstream.Edge(),
    llm=openai.LLM(model="gpt-5.4"),
    agent_user=User(name="Assistant", id="agent"),
    instructions="You have access to GitHub tools.",
    mcp_servers=[github_server]
)

Multiple Servers

agent = Agent(
    edge=getstream.Edge(),
    llm=llm,
    agent_user=User(name="Multi-Tool Assistant", id="agent"),
    mcp_servers=[github_server, weather_server, database_server]
)

Complete Example

import asyncio
import os
from vision_agents.core import Agent, User
from vision_agents.core.mcp import MCPServerRemote
from vision_agents.plugins import openai, getstream

async def main():
    github_server = MCPServerRemote(
        url="https://api.githubcopilot.com/mcp/",
        headers={"Authorization": f"Bearer {os.getenv('GITHUB_PAT')}"},
        timeout=10.0
    )

    llm = openai.LLM(model="gpt-5.4")

    @llm.register_function(description="Get current time")
    async def get_current_time() -> str:
        from datetime import datetime
        return datetime.now().strftime("%Y-%m-%d %H:%M:%S")

    agent = Agent(
        edge=getstream.Edge(),
        llm=llm,
        agent_user=User(name="GitHub Assistant", id="agent"),
        instructions="You can access GitHub and tell the time.",
        mcp_servers=[github_server]
    )

    await agent.create_user()
    call = agent.edge.client.video.call("default", "mcp-demo")

    async with agent.join(call):
        await agent.finish()

if __name__ == "__main__":
    asyncio.run(main())

Tool Execution Events

The framework emits events when tools execute:

Event	When	Fields
ToolStartEvent	Before tool runs	tool_name, arguments, tool_call_id
ToolEndEvent	After tool completes	tool_name, success, result/error, execution_time_ms

Multi-round tool calling

All LLM plugins support multiple tool-calling rounds. If the model needs to call more tools after seeing results, you can configure the maximum number of rounds (default 3):

# OpenAI Responses API
llm = openai.LLM(model="gpt-5.4", max_tool_rounds=5)

# ChatCompletions, Gemini, xAI, OpenRouter, and other plugins
llm = gemini.LLM(model="gemini-3-flash-preview", tools_max_rounds=5)

Next Steps

Model Context Protocol

MCP concepts

Create Your Own Plugin

Add function calling to custom LLMs