MCP 2.0¶

Overview¶

MCP 2.0 (2025-2026) brings major improvements for agent integration:

Streaming support (video, audio, large files)
Enhanced tool definitions
Better error handling
Performance optimizations

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Streaming Support¶

Video & Audio Streaming¶

class MCP2StreamingServer:
 """MCP 2.0 with streaming"""

 def register_streaming_tool(self, name: str, tool):
 """Register tool that returns streams"""

 tool_definition = {
 'name': name,
 'description': tool.description,
 'supportsStreaming': True,
 'streamOutputType': 'video' # or 'audio', 'binary'
 }

 self.tools[name] = tool_definition

 def stream_tool_result(self, tool_name: str, args: dict):
 """Stream large result"""

 tool = self.tools[tool_name]

 # For video/audio, don't buffer entire result
 # Stream chunks to client

 for chunk in tool.execute_streaming(args):
 yield {
 'type': 'stream_chunk',
 'chunk': chunk,
 'mimeType': 'video/mp4'
 }

 # Signal end
 yield {
 'type': 'stream_end',
 'success': True
 }

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Sampling Methods¶

Agent-Requested Capabilities¶

class MCP2Client:
 """MCP 2.0 client with sampling"""

 def request_sampling(self, request_type: str, params: dict):
 """Agent can request LLM sampling"""

 # Agent says: "I need your judgment on this"

 request = {
 'jsonrpc': '2.0',
 'method': 'sampling/create',
 'params': {
 'systemPrompt': params.get('system'),
 'messages': params.get('messages'),
 'model': params.get('model', 'claude-3-5-sonnet'),
 'maxTokens': params.get('max_tokens', 1000)
 }
 }

 # Server processes request
 response = self.send_request(request)

 # Agent gets model's sampling
 return response['result']['content']

Enhanced Tool Definitions¶

Better Schema Support¶

class MCP2ToolDefinition:
 """Improved tool definition format"""

 @staticmethod
 def define_complex_tool():
 """Tool with advanced features"""

 return {
 'name': 'analyze_data',
 'description': 'Analyze dataset with multiple options',

 # 2.0: Better schema support
 'inputSchema': {
 'type': 'object',
 'properties': {
 'data': {
 'type': 'array',
 'items': {'type': 'number'},
 'description': 'Data points to analyze'
 },
 'analysis_type': {
 'type': 'string',
 'enum': ['mean', 'median', 'stdev', 'correlation'],
 'description': 'Type of analysis'
 }
 },
 'required': ['data', 'analysis_type']
 },

 # 2.0: Output schema
 'outputSchema': {
 'type': 'object',
 'properties': {
 'result': {'type': 'number'},
 'confidence': {'type': 'number', 'minimum': 0, 'maximum': 1}
 }
 },

 # 2.0: Cost information
 'costInfo': {
 'estimatedTokens': 50,
 'estimatedCost': 0.001
 }
 }

Performance Improvements¶

Caching & Optimization¶

class MCP2PerformanceOptimization:
 """MCP 2.0 performance features"""

 def __init__(self):
 self.cache = Cache() # Result caching

 def execute_with_optimization(self, tool_name: str, args: dict):
 """Optimized execution"""

 # Check cache
 cache_key = self.make_key(tool_name, args)
 cached = self.cache.get(cache_key)

 if cached and not self.is_stale(cached):
 return cached['result']

 # Batch requests to same tool
 # (MCP 2.0 feature)

 # Execute
 result = self.execute_tool(tool_name, args)

 # Cache with TTL
 self.cache.set(cache_key, result, ttl=3600)

 return result

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Resource Protocol v2¶

Improved Resource Access¶

class MCP2ResourceProtocol:
 """Resource access in MCP 2.0"""

 def list_resources(self, uri_pattern: str = None):
 """List available resources"""

 request = {
 'jsonrpc': '2.0',
 'method': 'resources/list',
 'params': {
 'uriPattern': uri_pattern # Filter by pattern
 }
 }

 response = self.send_request(request)
 return response['result']['resources']

 def read_resource(self, uri: str):
 """Read resource content"""

 request = {
 'jsonrpc': '2.0',
 'method': 'resources/read',
 'params': {'uri': uri}
 }

 response = self.send_request(request)
 return response['result']['contents']

Migration from MCP 1.0¶

Backwards Compatibility¶

class MCP1To2Migration:
 """Upgrade from MCP 1.0"""

 @staticmethod
 def migrate_tool_definition(old_def):
 """Convert old tool to MCP 2.0"""

 new_def = {
 **old_def, # Keep existing fields

 # Add new 2.0 features
 'outputSchema': { # New
 'type': 'object'
 },
 'costInfo': { # New
 'estimatedTokens': 100
 },
 'supportsStreaming': False # New
 }

 return new_def

3 Warnings¶

Warning 1: Assuming 2.0 Everywhere¶

# WRONG
# Use MCP 2.0 features without checking version
result = server.stream_large_result()
# But server might be MCP 1.0!
# Breaks

# RIGHT
# Check server version first
version = server.get_protocol_version()
if version >= '2.0':
 result = server.stream_large_result()
else:
 result = server.get_buffered_result()

Warning 2: Streaming Overhead¶

# WRONG
# Stream everything
for result in stream_all_results():
 yield result

# Overhead for small results
# Better to batch

# RIGHT
# Stream only large results
if result.size > 1_000_000: # > 1MB
 stream_result(result)
else:
 buffer_and_send(result)

Warning 3: Sampling Loops¶

# WRONG
# Agent keeps calling sampling
while True:
 decision = agent.request_sampling()
 # Agent might loop forever

# RIGHT
# Limit sampling calls
max_samples = 5
for i in range(max_samples):
 decision = agent.request_sampling()
 if is_confident(decision):
 break

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Last Updated: August 9, 2026