Client Overview

ShotGrid MCP Server can be accessed by any client that implements the Model Context Protocol (MCP). This page provides an overview of the available client options and how to connect to your server.

Available Clients

Official MCP Clients

The official MCP Python SDK includes a client that can connect to any MCP server, including ShotGrid MCP Server:

from mcp.client import Client

async def main():
    # Connect to your ShotGrid MCP Server
    client = Client("http://localhost:8000")
    
    # List available tools
    tools = await client.list_tools()
    print(f"Available tools: {[tool.name for tool in tools]}")
    
    # Call a tool
    result = await client.call_tool("find_projects", {"status": "Active"})
    print(f"Active projects: {result}")

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

LLM Integrations

Many LLM platforms and frameworks support MCP natively or through plugins:

Anthropic Claude: Supports MCP natively through the API
OpenAI: Supports function calling which can be bridged to MCP
LangChain: Provides MCP tool integration
LlamaIndex: Supports MCP through its tool framework

Custom Clients

You can also create custom clients that implement the MCP protocol. The protocol is HTTP-based and relatively simple to implement.

Connecting to ShotGrid MCP Server

Basic Connection

To connect to a ShotGrid MCP Server, you need the server’s URL:

from mcp.client import Client

# Connect to a local server
client = Client("http://localhost:8000")

# Connect to a remote server
client = Client("https://shotgrid-mcp.example.com")

Authentication

If your server requires authentication, you can provide credentials:

from mcp.client import Client

# Connect with basic authentication
client = Client(
    "https://shotgrid-mcp.example.com",
    auth=("username", "password")
)

# Connect with token authentication
client = Client(
    "https://shotgrid-mcp.example.com",
    headers={"Authorization": "Bearer your-token"}
)

Connection Options

The MCP client supports various connection options:

from mcp.client import Client

# Connect with timeout settings
client = Client(
    "https://shotgrid-mcp.example.com",
    timeout=30.0  # 30 seconds timeout
)

# Connect with custom headers
client = Client(
    "https://shotgrid-mcp.example.com",
    headers={
        "User-Agent": "ShotGrid MCP Client/1.0",
        "X-Custom-Header": "Value"
    }
)

Using the Client

Listing Tools

To discover what tools are available on the server:

async def list_tools():
    client = Client("http://localhost:8000")
    tools = await client.list_tools()
    
    print(f"Found {len(tools)} tools:")
    for tool in tools:
        print(f"- {tool.name}: {tool.description}")
        print(f"  Parameters: {tool.parameters}")

if __name__ == "__main__":
    import asyncio
    asyncio.run(list_tools())

Calling Tools

To call a tool on the server:

async def find_shots():
    client = Client("http://localhost:8000")
    
    # Call the find_shots tool with parameters
    result = await client.call_tool(
        "find_shots",
        {
            "project_id": 123,
            "status": "ip"
        }
    )
    
    print(f"Found {len(result)} shots:")
    for shot in result:
        print(f"- {shot['code']}: {shot['sg_status_list']}")

if __name__ == "__main__":
    import asyncio
    asyncio.run(find_shots())

Reading Resources

To read a resource from the server:

async def read_thumbnail():
    client = Client("http://localhost:8000")
    
    # Get a thumbnail URL
    thumbnail_info = await client.call_tool(
        "get_thumbnail_url",
        {
            "entity_type": "Asset",
            "entity_id": 456
        }
    )
    
    # Read the thumbnail resource
    resource = await client.read_resource(thumbnail_info["url"])
    
    # Save the thumbnail to a file
    with open("thumbnail.jpg", "wb") as f:
        f.write(resource[0].content)
    
    print(f"Saved thumbnail to thumbnail.jpg")

if __name__ == "__main__":
    import asyncio
    asyncio.run(read_thumbnail())

Error Handling

Handle errors from the server:

from mcp.errors import MCPError, ToolExecutionError

async def handle_errors():
    client = Client("http://localhost:8000")
    
    try:
        # Call a tool with invalid parameters
        result = await client.call_tool(
            "update_shot",
            {
                "shot_id": 999999,  # Non-existent shot
                "status": "invalid_status"
            }
        )
    except ToolExecutionError as e:
        print(f"Tool execution failed: {e.message}")
        print(f"Error details: {e.details}")
    except MCPError as e:
        print(f"MCP protocol error: {e}")
    except Exception as e:
        print(f"Unexpected error: {e}")

if __name__ == "__main__":
    import asyncio
    asyncio.run(handle_errors())

Integrating with LLMs

Anthropic Claude

Integrate with Anthropic Claude:

import anthropic
from mcp.client import Client

async def claude_with_mcp():
    # Connect to ShotGrid MCP Server
    mcp_client = Client("http://localhost:8000")
    
    # List available tools
    tools = await mcp_client.list_tools()
    
    # Create Claude client
    claude = anthropic.Anthropic(api_key="your-api-key")
    
    # Create a message with tools
    message = claude.messages.create(
        model="claude-3-opus-20240229",
        max_tokens=1000,
        system="You are a helpful assistant with access to ShotGrid data.",
        messages=[
            {"role": "user", "content": "Find all in-progress shots in the Awesome Film project."}
        ],
        tools=tools  # Claude can use MCP tools directly
    )
    
    # Process tool calls
    for tool_call in message.tool_calls:
        tool_result = await mcp_client.call_tool(
            tool_call.name,
            tool_call.parameters
        )
        
        # Send the tool result back to Claude
        message = claude.messages.create(
            model="claude-3-opus-20240229",
            max_tokens=1000,
            system="You are a helpful assistant with access to ShotGrid data.",
            messages=[
                {"role": "user", "content": "Find all in-progress shots in the Awesome Film project."},
                {"role": "assistant", "content": message.content, "tool_calls": message.tool_calls},
                {"role": "user", "content": "", "tool_results": [
                    {"tool_call_id": tool_call.id, "result": tool_result}
                ]}
            ]
        )
    
    print(message.content)

if __name__ == "__main__":
    import asyncio
    asyncio.run(claude_with_mcp())

OpenAI

Integrate with OpenAI:

from openai import OpenAI
from mcp.client import Client

async def openai_with_mcp():
    # Connect to ShotGrid MCP Server
    mcp_client = Client("http://localhost:8000")
    
    # List available tools and convert to OpenAI format
    mcp_tools = await mcp_client.list_tools()
    openai_tools = [
        {
            "type": "function",
            "function": {
                "name": tool.name,
                "description": tool.description,
                "parameters": tool.parameters
            }
        }
        for tool in mcp_tools
    ]
    
    # Create OpenAI client
    openai_client = OpenAI(api_key="your-api-key")
    
    # Create a chat completion with tools
    response = openai_client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are a helpful assistant with access to ShotGrid data."},
            {"role": "user", "content": "Find all in-progress shots in the Awesome Film project."}
        ],
        tools=openai_tools
    )
    
    # Process tool calls
    message = response.choices[0].message
    for tool_call in message.tool_calls or []:
        import json
        tool_name = tool_call.function.name
        tool_params = json.loads(tool_call.function.arguments)
        
        # Call the MCP tool
        tool_result = await mcp_client.call_tool(tool_name, tool_params)
        
        # Send the tool result back to OpenAI
        response = openai_client.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are a helpful assistant with access to ShotGrid data."},
                {"role": "user", "content": "Find all in-progress shots in the Awesome Film project."},
                {"role": "assistant", "content": message.content, "tool_calls": message.tool_calls},
                {"role": "tool", "tool_call_id": tool_call.id, "content": json.dumps(tool_result)}
            ]
        )
    
    print(response.choices[0].message.content)

if __name__ == "__main__":
    import asyncio
    asyncio.run(openai_with_mcp())

Next Steps

Now that you understand how to connect to ShotGrid MCP Server, you can:

Learn about the Python Client in more detail
Explore optimized queries for better performance
See how to handle errors gracefully

Get Started

Server

Clients

AI Integration

Development

Patterns

Client Overview

Client Overview

Available Clients

Official MCP Clients

LLM Integrations

Custom Clients

Connecting to ShotGrid MCP Server

Basic Connection

Authentication

Connection Options

Using the Client

Listing Tools

Calling Tools

Reading Resources

Error Handling

Integrating with LLMs

Anthropic Claude

OpenAI

Next Steps

Get Started

Server

Clients

AI Integration

Development

Patterns

​Client Overview

​Available Clients

​Official MCP Clients

​LLM Integrations

​Custom Clients

​Connecting to ShotGrid MCP Server

​Basic Connection

​Authentication

​Connection Options

​Using the Client

​Listing Tools

​Calling Tools

​Reading Resources

​Error Handling

​Integrating with LLMs

​Anthropic Claude

​OpenAI

​Next Steps

Client Overview

Available Clients

Official MCP Clients

LLM Integrations

Custom Clients

Connecting to ShotGrid MCP Server

Basic Connection

Authentication

Connection Options

Using the Client

Listing Tools

Calling Tools

Reading Resources

Error Handling

Integrating with LLMs

Anthropic Claude

OpenAI

Next Steps