client.py
"""Agent with MCP tools using Dynamic Headers"""
import asyncio
from typing import TYPE_CHECKING, Optional
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.run import RunContext
from agno.tools.mcp import MCPTools
if TYPE_CHECKING:
from agno.agent import Agent
from agno.team import Team
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
async def main():
"""Example showing dynamic headers with different users."""
# Step 1: Define your header provider
# This function can receive RunContext, Agent and/or Team based on its signature
def header_provider(
run_context: RunContext,
agent: Optional["Agent"] = None,
team: Optional["Team"] = None,
) -> dict:
"""
Generate dynamic headers from RunContext and Agent.
The header_provider can accept any combination of these parameters:
- run_context: The RunContext for the current agent or team run
- agent: The contextual Agent instance
- team: The contextual Team instance
The RunContext contains:
- run_id: Unique ID for this agent run
- user_id: User ID passed to agent.arun()
- session_id: Session ID passed to agent.arun()
- metadata: Dict of custom metadata passed to agent.arun()
"""
headers = {
"X-User-ID": run_context.user_id or "unknown",
"X-Session-ID": run_context.session_id or "unknown",
"X-Run-ID": run_context.run_id,
"X-Tenant-ID": run_context.metadata.get("tenant_id", "no-tenant")
if run_context.metadata
else "no-tenant",
# You can also access agent and team properties if needed
"X-Agent-Name": agent.name
if agent
else team.name
if team
else "unnamed-agno-entity",
}
return headers
# Step 2: Create MCPTools with header_provider
# This enables dynamic headers for all MCP tool calls
mcp_tools = MCPTools(
url="http://localhost:8000/mcp", # Your MCP server URL
transport="streamable-http", # Use streamable-http or sse for headers
header_provider=header_provider, # This enables dynamic headers!
)
# Step 3: Connect to MCP server
await mcp_tools.connect()
print("Connected to MCP server")
print(f" Available tools: {list(mcp_tools.functions.keys())}\n")
try:
# Step 4: Create agent with MCP tools
agent_1 = Agent(
name="agent-1",
model=OpenAIChat(id="gpt-5.2"),
tools=[mcp_tools],
markdown=False,
)
# Step 5: Run agent with different users
# The agent automatically creates RunContext and injects it into tools!
# Example 1: User "neel"
print("=" * 60)
print("Example 1: Running as user 'neel'")
print("=" * 60)
response1 = await agent_1.arun(
"Please use the greet tool to greet me. My name is neel.",
user_id="neel", # ← Goes into RunContext.user_id
session_id="session-1", # ← Goes into RunContext.session_id
metadata={ # ← Goes into RunContext.metadata
"tenant_id": "tenant-1",
},
)
print(f"Response: {response1.content}\n")
# Example 2: User "dirk"
print("=" * 60)
print("Example 2: Running as user 'dirk'")
print("=" * 60)
agent_2 = Agent(
name="agent-2",
model=OpenAIChat(id="gpt-5.2"),
tools=[mcp_tools],
markdown=False,
)
response2 = await agent_2.arun(
"Please use the greet tool to greet me. My name is dirk.",
user_id="dirk", # Different user!
session_id="session-2", # Different session!
metadata={
"tenant_id": "tenant-2", # Different tenant!
},
)
print(f"Response: {response2.content}\n")
print("=" * 60)
print("Success! Check your MCP server logs to see the headers.")
print("=" * 60)
finally:
# Step 6: Clean up
await mcp_tools.close()
print("\nConnection closed")
# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
asyncio.run(main())
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U "agno[mcp]" openai
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run the example
Save the code above as
client.py, then run:python client.py