""" MCP Server Implementation for Future Earth Demonstrates real MCP protocol integration for heavy compute operations """ import json from typing import Dict, List, Optional import anthropic class MCPVisionServer: """ MCP Server for vision analysis In production, this would be a separate service """ def __init__(self, api_key: str): self.client = anthropic.Anthropic(api_key=api_key) self.name = "vision_analysis_mcp" self.version = "1.0.0" def analyze_product_image(self, image_base64: str, query: str) -> Dict: """ MCP Tool: Analyze product images for materials and environmental concerns """ try: message = self.client.messages.create( model="claude-sonnet-4-20250514", max_tokens=1000, messages=[ { "role": "user", "content": [ { "type": "image", "source": { "type": "base64", "media_type": "image/jpeg", "data": image_base64, }, }, { "type": "text", "text": f"""Analyze this product image for environmental assessment. Query: {query} Identify: 1. Product type and category 2. Materials used (plastic, metal, fabric, etc.) 3. Packaging materials 4. Visible sustainability markers (recycling symbols, certifications) 5. Single-use vs reusable nature Respond in JSON format.""" } ] } ] ) response_text = message.content[0].text # Parse response if "```json" in response_text: response_text = response_text.split("```json")[1].split("```")[0].strip() analysis = json.loads(response_text) return { "mcp_server": self.name, "tool": "analyze_product_image", "status": "success", "analysis": analysis, "confidence": 0.85 } except Exception as e: return { "mcp_server": self.name, "tool": "analyze_product_image", "status": "error", "error": str(e) } def detect_greenwashing(self, product_claims: List[str]) -> Dict: """ MCP Tool: Detect potential greenwashing in product claims """ prompt = f"""Analyze these product claims for potential greenwashing: Claims: {json.dumps(product_claims)} Evaluate each claim for: 1. Specificity (vague vs concrete) 2. Verifiability (can it be proven?) 3. Relevance (does it matter environmentally?) 4. Common greenwashing patterns Return JSON with risk score (1-10) for each claim.""" try: message = self.client.messages.create( model="claude-sonnet-4-20250514", max_tokens=1500, messages=[{"role": "user", "content": prompt}] ) response_text = message.content[0].text if "```json" in response_text: response_text = response_text.split("```json")[1].split("```")[0].strip() return { "mcp_server": self.name, "tool": "detect_greenwashing", "status": "success", "analysis": json.loads(response_text) } except: return { "mcp_server": self.name, "tool": "detect_greenwashing", "status": "error" } class MCPReasoningServer: """ MCP Server for complex environmental reasoning and calculations """ def __init__(self, api_key: str): self.client = anthropic.Anthropic(api_key=api_key) self.name = "environmental_reasoning_mcp" self.version = "1.0.0" def calculate_lifecycle_impact(self, product_data: Dict) -> Dict: """ MCP Tool: Comprehensive lifecycle impact analysis """ prompt = f"""Perform a detailed Life Cycle Assessment (LCA) for this product: Product Data: {json.dumps(product_data, indent=2)} Calculate and reason about: 1. Raw material extraction impact 2. Manufacturing carbon footprint 3. Transportation emissions 4. Usage phase impact 5. End-of-life disposal/recycling Use standard LCA methodology (ISO 14040/14044). Provide numerical estimates where possible. Return as JSON with reasoning chain.""" try: message = self.client.messages.create( model="claude-sonnet-4-20250514", max_tokens=2000, messages=[{"role": "user", "content": prompt}] ) response_text = message.content[0].text if "```json" in response_text: response_text = response_text.split("```json")[1].split("```")[0].strip() return { "mcp_server": self.name, "tool": "calculate_lifecycle_impact", "status": "success", "analysis": json.loads(response_text) } except Exception as e: return { "mcp_server": self.name, "tool": "calculate_lifecycle_impact", "status": "error", "error": str(e) } def compare_alternatives(self, original: Dict, alternatives: List[Dict]) -> Dict: """ MCP Tool: Multi-criteria comparison of product alternatives """ prompt = f"""Compare these product alternatives across environmental criteria: Original Product: {json.dumps(original)} Alternatives: {json.dumps(alternatives)} Evaluate on: - Carbon footprint reduction - Resource efficiency - Circular economy potential - Cost-benefit analysis - Practicality for average consumer Rank alternatives and provide recommendation scores. Return as JSON.""" try: message = self.client.messages.create( model="claude-sonnet-4-20250514", max_tokens=2000, messages=[{"role": "user", "content": prompt}] ) response_text = message.content[0].text if "```json" in response_text: response_text = response_text.split("```json")[1].split("```")[0].strip() return { "mcp_server": self.name, "tool": "compare_alternatives", "status": "success", "comparison": json.loads(response_text) } except: return { "mcp_server": self.name, "tool": "compare_alternatives", "status": "error" } class MCPParsingServer: """ MCP Server for parsing environmental reports and certifications """ def __init__(self, api_key: str): self.client = anthropic.Anthropic(api_key=api_key) self.name = "data_parsing_mcp" self.version = "1.0.0" def parse_environmental_report(self, report_text: str) -> Dict: """ MCP Tool: Extract structured data from environmental reports """ prompt = f"""Parse this environmental report and extract key metrics: Report Text: {report_text} Extract: 1. Carbon footprint values (with units) 2. Water usage metrics 3. Waste generation 4. Energy consumption 5. Certifications mentioned 6. Improvement targets Return structured JSON with all metrics normalized to standard units.""" try: message = self.client.messages.create( model="claude-sonnet-4-20250514", max_tokens=2000, messages=[{"role": "user", "content": prompt}] ) response_text = message.content[0].text if "```json" in response_text: response_text = response_text.split("```json")[1].split("```")[0].strip() return { "mcp_server": self.name, "tool": "parse_environmental_report", "status": "success", "parsed_data": json.loads(response_text) } except: return { "mcp_server": self.name, "tool": "parse_environmental_report", "status": "error" } def verify_certifications(self, certifications: List[str]) -> Dict: """ MCP Tool: Verify environmental certifications and their credibility """ prompt = f"""Verify these environmental certifications: Certifications: {json.dumps(certifications)} For each certification, provide: 1. Validity (legitimate vs greenwashing) 2. Issuing organization credibility 3. Standards required to obtain 4. What it actually guarantees 5. Credibility score (1-10) Return as JSON.""" try: message = self.client.messages.create( model="claude-sonnet-4-20250514", max_tokens=1500, messages=[{"role": "user", "content": prompt}] ) response_text = message.content[0].text if "```json" in response_text: response_text = response_text.split("```json")[1].split("```")[0].strip() return { "mcp_server": self.name, "tool": "verify_certifications", "status": "success", "verification": json.loads(response_text) } except: return { "mcp_server": self.name, "tool": "verify_certifications", "status": "error" } class MCPOrchestrator: """ Orchestrates MCP server calls for the EcoAgent """ def __init__(self, api_key: str): self.vision_server = MCPVisionServer(api_key) self.reasoning_server = MCPReasoningServer(api_key) self.parsing_server = MCPParsingServer(api_key) self.servers = { "vision": self.vision_server, "reasoning": self.reasoning_server, "parsing": self.parsing_server } def call_mcp_tool(self, server_name: str, tool_name: str, **kwargs) -> Dict: """ Generic MCP tool caller """ server = self.servers.get(server_name) if not server: return {"error": f"MCP server '{server_name}' not found"} tool_method = getattr(server, tool_name, None) if not tool_method: return {"error": f"Tool '{tool_name}' not found on server '{server_name}'"} return tool_method(**kwargs) def get_available_tools(self) -> Dict: """ List all available MCP tools """ return { "vision": { "server": self.vision_server.name, "tools": ["analyze_product_image", "detect_greenwashing"] }, "reasoning": { "server": self.reasoning_server.name, "tools": ["calculate_lifecycle_impact", "compare_alternatives"] }, "parsing": { "server": self.parsing_server.name, "tools": ["parse_environmental_report", "verify_certifications"] } } # Example usage if __name__ == "__main__": import os api_key = os.environ.get("ANTHROPIC_API_KEY") orchestrator = MCPOrchestrator(api_key) # List available tools print("Available MCP Tools:") print(json.dumps(orchestrator.get_available_tools(), indent=2)) # Example: Calculate lifecycle impact product_data = { "name": "Plastic Water Bottle", "materials": ["PET plastic", "Plastic cap"], "weight": "30g" } result = orchestrator.call_mcp_tool( "reasoning", "calculate_lifecycle_impact", product_data=product_data ) print("\nLifecycle Impact Analysis:") print(json.dumps(result, indent=2))