mcpbuilder-ai 1.0.1

Python SDK for MCPBuilder - MCP agent integration with chat interface

MCPBuilder Python SDK

A Python SDK for integrating MCPBuilder's MCP agent chat service into your applications.

Overview

The MCPBuilder Python SDK provides a simple, callback-based interface to connect to the MCP client service and interact with AI agents that can execute tools via the Model Context Protocol (MCP).

Features

• WebSocket-based real-time communication - Stream tokens and events in real-time
• Callback-based event handling - Clean fluent API for registering event handlers
• Tool consent handling - Allow or deny tool executions programmatically
• Automatic reconnection - Handle connection drops gracefully with exponential backoff
• Session management - Create, reuse, and manage chat sessions
• Type-safe - Full type hints for IDE support

Requirements

• Python 3.10+
• A valid project_token for authentication
• A valid deployment_name configured in your MCPBuilder setup

Installation

Using pip

pip install mcpbuilder-ai

Using Poetry

poetry add mcpbuilder-ai

From Source

git clone <repository-url>
cd python-sdk
poetry install

Quick Start

1. Environment Setup

Set the required environment variables:

export PROJECT_TOKEN=your-project-token               # Authentication token
export DEPLOYMENT_NAME=your-deployment                # Deployment identifier

Note: The SDK automatically converts http:// to ws:// and https:// to wss:// for WebSocket connections.

2. Basic Usage

import asyncio
import os
from mcpbuilder import MCPChatClient


async def main():
    # Create client - pass all configuration at initialization
    client = MCPChatClient(
        project_token=os.getenv("PROJECT_TOKEN"),
        deployment_name=os.getenv("DEPLOYMENT_NAME"),
        cache_history=False,  # Set to True to persist history on server
        # Optional: provide security params that override loaded credentials
        # security_params={
        #     "X-API-KEY": os.getenv("MY_API_KEY"),
        #     "Authorization": f"Bearer {os.getenv('MY_ACCESS_TOKEN')}",
        # }
    )
    
    # Register callbacks to handle events
    client \
        .on_token(lambda text: print(text, end="", flush=True)) \
        .on_final(lambda text, tool_calls: print("\n\n✓ Done")) \
        .on_error(lambda msg: print(f"\n❌ Error: {msg}"))
    
    # Connect - all configuration already set
    await client.connect()
    
    # Send a message
    await client.send_message("Hello! What can you help me with?")
    
    # Wait for response (in real app, use proper event handling)
    await asyncio.sleep(30)
    
    # Disconnect when done
    await client.disconnect()


asyncio.run(main())

3. Security Parameters

The SDK supports passing security parameters (API keys, tokens, etc.) at initialization. These parameters are merged with credentials loaded from the chatbot settings, with SDK parameters taking precedence:

# Provide your own credentials that don't need to be fetched from secret service
client = MCPChatClient(
    project_token=os.getenv("PROJECT_TOKEN"),
    deployment_name="my-deployment",
    cache_history=False,  # Set to True to persist history on server
    security_params={
        # Header name -> value pairs that will be sent to the MCP server
        "X-API-KEY": os.getenv("MY_EXTERNAL_API_KEY"),
        "Authorization": f"Bearer {os.getenv('MY_OAUTH_TOKEN')}",
        "X-Custom-Header": "custom-value",
    }
)

This is useful when:

• You have credentials available locally (e.g., from environment variables)
• You want to override credentials loaded from MCPBuilder settings
• You need to provide credentials that aren't stored in the secret service

3. Interactive Chat Example

import asyncio
import os
from mcpbuilder import MCPChatClient


async def main():
    client = MCPChatClient(
        project_token=os.getenv("PROJECT_TOKEN"),
        deployment_name=os.getenv("DEPLOYMENT_NAME"),
        cache_history=False,  # Set to True to persist history on server
    )
    
    # Track when response is complete
    response_done = asyncio.Event()
    
    # Register all callbacks
    client \
        .on_connected(lambda sid: print(f"✓ Connected (session: {sid})")) \
        .on_token(lambda text: print(text, end="", flush=True)) \
        .on_tool_start(lambda name, args: print(f"\n🔧 Calling: {name}")) \
        .on_tool_end(lambda name, output, ok, err: print(f"\n{'✓' if ok else '✗'} {name}")) \
        .on_final(lambda text, tool_calls: response_done.set()) \
        .on_error(lambda msg: print(f"\n❌ {msg}")) \
        .on_disconnected(lambda reason: print(f"\n🔌 Disconnected: {reason}"))
    
    await client.connect(timezone="Europe/Berlin")
    
    print("\nType your message (or 'quit' to exit):\n")
    
    while True:
        user_input = input("You: ")
        
        if user_input.lower() == "quit":
            break
        
        response_done.clear()
        print("\nAssistant: ", end="", flush=True)
        
        await client.send_message(user_input)
        await asyncio.wait_for(response_done.wait(), timeout=120)
        print()
    
    await client.disconnect()


asyncio.run(main())

4. Tool Consent Handling

When the agent wants to execute tools, you can control approval:

import asyncio
import os
from mcpbuilder import MCPChatClient


async def main():
    client = MCPChatClient(
        project_token=os.getenv("PROJECT_TOKEN"),
        deployment_name="my-deployment",
        cache_history=False,  # Set to True to persist history on server
    )
    
    # Define consent handler
    async def handle_consent(consent_id: str, tool: str, args: dict, description: str):
        print(f"\n🔐 Tool '{tool}' requests permission")
        print(f"   Arguments: {args}")
        
        # Option 1: Auto-approve specific tools
        if tool in ["search", "get_time", "list_files"]:
            await client.respond_consent(consent_id, allow=True)
            return
        
        # Option 2: Ask user
        response = input("   Allow? (y/n/a=allow all): ").lower()
        
        if response == "a":
            await client.respond_consent(consent_id, allow=True, allow_all=True)
        elif response == "y":
            await client.respond_consent(consent_id, allow=True)
        else:
            await client.respond_consent(consent_id, allow=False)
    
    # Register the consent callback
    client.on_tool_consent_request(
        lambda cid, tool, args, desc: asyncio.create_task(
            handle_consent(cid, tool, args, desc)
        )
    )
    
    # ... rest of setup
    await client.connect()


asyncio.run(main())

4. Auto-Approve All Tools

If you trust all tools in your deployment:

# After connecting, enable auto-consent
await client.connect()  # deployment_name is set in constructor
await client.set_consent_all(enabled=True)

# Or allow specific tools only
await client.allow_tool("search")
await client.allow_tool("get_weather")

Configuration

The SDK uses environment variables for configuration, with optional explicit overrides.

Environment Variables

Variable	Required	Description
PROJECT_TOKEN	Yes	Authentication token for your project
DEPLOYMENT_NAME	Yes	Your deployment identifier
TIMEZONE	No	Client timezone (default: UTC)

Configuration Examples

Standard usage (environment variables):

import os
from mcpbuilder import MCPChatClient

client = MCPChatClient(
    project_token=os.getenv("PROJECT_TOKEN"),
    deployment_name=os.getenv("DEPLOYMENT_NAME"),
    cache_history=False,            # Set to True to persist history on server
    auto_reconnect=True,
    max_reconnect_attempts=5,
)
await client.connect(timezone="Europe/Berlin")

On-premise deployment (explicit URL):

import os
from mcpbuilder import MCPChatClient

# For on-premise installations, pass the URL explicitly
client = MCPChatClient(
    project_token=os.getenv("PROJECT_TOKEN"),
    deployment_name=os.getenv("DEPLOYMENT_NAME"),
    cache_history=False,  # Set to True to persist history on server
)
await client.connect()

### Client Options

```python
import os
from mcpbuilder import MCPChatClient

client = MCPChatClient(
    project_token=os.getenv("PROJECT_TOKEN"),  # Required: Authentication token
    deployment_name=os.getenv("DEPLOYMENT_NAME"),  # Required: Deployment identifier
    cache_history=False,                        # Cache history on server (default: False)
    auto_reconnect=True,                        # Enable automatic reconnection (default: True)
    max_reconnect_attempts=5,                   # Max reconnection attempts (default: 5)
    reconnect_delay_ms=2000,                    # Base delay for exponential backoff (default: 2000)
)

Connect Options

await client.connect(
    timezone="Europe/Berlin",           # Optional: Client timezone
)

Note: deployment_name, message_history, and cache_history are configured in the constructor.

Message History

You can provide default conversation history in the constructor, or override history per message. This is useful for resuming conversations or providing context:

from mcpbuilder import MCPChatClient, HistoryMessage

# Create history messages
history = [
    HistoryMessage(role="user", content="What is the capital of France?"),
    HistoryMessage(role="assistant", content="The capital of France is Paris."),
]

client = MCPChatClient(
    project_token=os.getenv("PROJECT_TOKEN"),
    deployment_name="my-deployment",
    message_history=history,
    cache_history=False,  # Set to True to persist history on server
)

# Connect (history is already configured)
await client.connect()

# Optional: override history for this specific message
await client.send_message("What about Germany?", message_history=history)

The history is sent to the server and prepended to the conversation after the system message, before any new messages. Messages are converted to the appropriate LangChain message types (HumanMessage/AIMessage) on the server.

LLM Configuration

You can configure the LLM behavior using setter methods before connecting:

import os
from mcpbuilder import MCPChatClient

client = MCPChatClient(
    project_token=os.getenv("PROJECT_TOKEN"),
    deployment_name="my-deployment",
    cache_history=False,  # Set to True to persist history on server
)

# Configure LLM system message and language
client \
    .set_system_message("You are an expert assistant in Olympic sports. Answer questions accurately and concisely.") \
    .set_language("English")

# Then connect
await client.connect()

API Reference

MCPChatClient

The main client class for interacting with the MCP service.

Constructor

MCPChatClient(
    project_token: str,                 # Required: Authentication token
    deployment_name: str,               # Required: Deployment identifier
    security_params: dict | None = None,# Optional: Security headers to merge
    system_message: str | None = None,  # Optional: System prompt
    language: str | None = None,        # Optional: Assistant language
    message_history: List[HistoryMessage] | None = None,  # Optional: Initial history
    cache_history: bool = False,        # Optional: Cache history on server
    auto_reconnect: bool = True,        # Enable auto-reconnect
    max_reconnect_attempts: int = 5,    # Max retry attempts
    reconnect_delay_ms: int = 2000,     # Base delay (exponential backoff)
)

Connection Methods

Method	Description
connect(timezone?)	Connect to the service
disconnect()	Disconnect from the service
is_connected	Property: Check connection status
session_id	Property: Current session ID (after connect)

Configuration Methods

Method	Description
set_system_message(message)	Set system message/prompt for the LLM
set_language(language)	Set language for the assistant

Message Methods

Method	Description
send_message(text, message_history?)	Send a chat message
stop()	Stop the current agent run

Consent Methods

Method	Description
respond_consent(consent_id, allow, allow_all?)	Respond to consent request
set_consent_all(enabled)	Enable/disable auto-consent for all tools
allow_tool(tool_name)	Auto-allow a specific tool

Callback Registration (Fluent API)

All callback methods return self for chaining:

client \
    .on_connected(callback) \
    .on_token(callback) \
    .on_tool_start(callback) \
    .on_tool_end(callback) \
    .on_tool_consent_request(callback) \
    .on_consent_timeout(callback) \
    .on_final(callback) \
    .on_error(callback) \
    .on_status(callback) \
    .on_disconnected(callback)

Callback Signatures

Callback	Signature	Description
on_connected	(session_id: str) -> None	Connection established
on_token	(text: str) -> None	Streaming token received
on_tool_start	(name: str, input: dict) -> None	Tool execution starting
on_tool_end	(name: str, output: str, success: bool, error: Optional[str]) -> None	Tool execution completed
on_tool_consent_request	(consent_id: str, tool: str, args: dict, description: Optional[str]) -> None	Tool consent needed
on_consent_timeout	(consent_id: str) -> None	Consent request timed out
on_final	(text: str, tool_calls: List[Dict[str, Any]]) -> None	Final complete response with tool calls
on_error	(message: str) -> None	Error occurred
on_status	(message: str) -> None	Status update
on_disconnected	(reason: Optional[str]) -> None	Disconnected

Server Event Types

The SDK handles these event types from the server:

Event Type	Description
status	Status update (including initialized with session_id)
token	Streaming text token from agent response
tool_start	Tool invocation started
tool_end_success	Tool completed successfully
tool_end_failure	Tool failed
tool_denied	Tool was denied by user
tool_error	Tool execution error
tool_consent_request	Request for tool consent
consent_timeout	Consent request timed out
final	Final complete response
error	Error occurred
closing	Server closing connection
connection_closed	Connection closed

Examples

See the examples/ directory for complete examples:

Example	Description
callback_chat.py	Interactive chat with MCPChatClient
cli_chat.py	Command-line chat interface
service_integration.py	Programmatic usage patterns

Running Examples

cd python-sdk

# Install dependencies
poetry install

# Set required environment variables
export PROJECT_TOKEN=your-project-token
export DEPLOYMENT_NAME=my-deployment

# Run interactive chat
poetry run python examples/callback_chat.py

Error Handling

Connection Errors

The SDK automatically handles reconnection with exponential backoff:

import os
from mcpbuilder import MCPChatClient

client = MCPChatClient(
    project_token=os.getenv("PROJECT_TOKEN"),
    deployment_name=os.getenv("DEPLOYMENT_NAME"),
    cache_history=False,        # Set to True to persist history on server
    auto_reconnect=True,
    max_reconnect_attempts=5,
    reconnect_delay_ms=2000,  # 2s, 4s, 6s, 8s, 10s
)

client.on_error(lambda msg: print(f"Error: {msg}"))
client.on_disconnected(lambda reason: print(f"Disconnected: {reason}"))

### Handling Specific Errors

```python
client.on_error(lambda msg: handle_error(msg))

def handle_error(message: str):
    if "authentication" in message.lower():
        print("Check your deployment_name")
    elif "timeout" in message.lower():
        print("Request timed out, try again")
    else:
        print(f"Error: {message}")

Development

Setup Development Environment

cd python-sdk
poetry install --with dev

Run Tests

poetry run pytest

Code Quality

# Format
poetry run black src/ examples/

# Lint
poetry run ruff check src/ examples/

# Type check
poetry run mypy src/

Architecture

┌─────────────────┐         ┌─────────────────────┐
│  Your App       │         │  mcp-client-service │
│                 │   WS    │                     │
│  MCPChatClient ◄──────────►  WebSocket Server   │
│                 │         │                     │
│  - callbacks    │         │  - Sessions         │
│  - send_message │         │  - Agent Runner     │
│  - consent      │         │  - Tool Execution   │
└─────────────────┘         └─────────────────────┘

Troubleshooting

"Connection failed" on connect

1. Verify the mcp-client-service is running
2. Check the URL is correct (ws:// or wss://)
3. Verify the deployment_name exists

No events received

1. Ensure callbacks are registered before connect()
2. Check for errors with on_error() callback
3. Verify the session initialized with on_connected() callback

Tool consent not working

1. Ensure on_tool_consent_request() callback is registered
2. Use asyncio.create_task() for async consent handlers
3. Call respond_consent() within the timeout period

License

MIT