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| """ | |
| 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)) |