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