R Econometrics MCP Server

Validated Working Examples ========================= **Live Server**: https://rmcp-server-394229601724.us-central1.run.app These examples have been tested against the live RMCP server and are guaranteed to work. Business Performance Analysis ----------------------------- This example analyzes the relationship between sales performance and business metrics. **Sample Dataset:** - **Sales**: Monthly sales revenue ($) - **Marketing Spend**: Monthly marketing budget ($) - **Customer Satisfaction**: Customer satisfaction scores (1-10) - **Website Visits**: Monthly website visitors Step 1: Initialize Session ~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash curl -X POST https://rmcp-server-394229601724.us-central1.run.app/mcp \ -H "Content-Type: application/json" \ -H "MCP-Protocol-Version: 2025-06-18" \ -d '{ "jsonrpc": "2.0", "id": 1, "method": "initialize", "params": { "protocolVersion": "2025-06-18", "capabilities": {}, "clientInfo": { "name": "business-analyst", "version": "1.0" } } }' **Response:** .. code-block:: json { "jsonrpc": "2.0", "id": 1, "result": { "protocolVersion": "2025-06-18", "capabilities": { "tools": {"listChanged": false}, "resources": {"subscribe": true, "listChanged": true}, "prompts": {"listChanged": false}, "logging": {}, "completion": {} }, "serverInfo": { "name": "RMCP MCP Server", "version": "0.7.0" } } } ⚠️ **Important**: Extract the ``MCP-Session-Id`` from response headers and use in subsequent requests. Step 2: Linear Regression Analysis ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Analyze how marketing spend and customer satisfaction impact sales. .. code-block:: bash curl -X POST https://rmcp-server-394229601724.us-central1.run.app/mcp \ -H "Content-Type: application/json" \ -H "MCP-Protocol-Version: 2025-06-18" \ -H "MCP-Session-Id: YOUR_SESSION_ID" \ -d '{ "jsonrpc": "2.0", "id": 2, "method": "tools/call", "params": { "name": "linear_model", "arguments": { "formula": "sales ~ marketing_spend + customer_satisfaction", "data": { "sales": [15000, 18000, 12000, 22000, 16000, 20000, 14000, 25000, 13000, 19000], "marketing_spend": [2000, 2500, 1500, 3000, 2200, 2800, 1800, 3500, 1600, 2600], "customer_satisfaction": [8.5, 8.8, 7.2, 9.1, 8.0, 8.9, 7.8, 9.3, 7.5, 8.7] }, "family": "gaussian" } } }' **Actual Response (Verified):** .. code-block:: json { "jsonrpc": "2.0", "id": 2, "result": { "content": [ { "type": "text", "text": "## Linear Regression Results\n\n**Model Statistics:**\n- R² = 0.995\n- Adjusted R² = 0.9935\n- F-statistic = 693.12 (p < 0.001)\n\n### Coefficients\n\n|term | estimate| std.error| statistic| p.value| conf.low| conf.high|\n|:---------------------|---------:|---------:|---------:|-------:|----------:|----------:|\n|(Intercept) | 4376.9072| 2752.7349| 1.5900| 0.1559| -2132.2766| 10886.0910|\n|marketing_spend | 6.7903| 0.5045| 13.4608| 0.0000| 5.5975| 7.9832|\n|customer_satisfaction | -350.1367| 457.5716| -0.7652| 0.4692| -1432.1216| 731.8481|", "annotations": { "mimeType": "text/markdown" } } ], "structuredContent": { "type": "json", "json": { "coefficients": { "(Intercept)": 4376.9072, "marketing_spend": 6.7903, "customer_satisfaction": -350.1367 }, "r_squared": 0.995, "adj_r_squared": 0.9935, "f_statistic": 693.1181, "f_p_value": 8.9899e-9 } } } } **Key Findings:** - **Strong Model**: R² = 0.995 (explains 99.5% of sales variation) - **Marketing ROI**: Every $1 in marketing generates $6.79 in sales - **Customer Satisfaction**: Not statistically significant (p = 0.47) - **Model Significance**: F-statistic = 693.12 (p < 0.001) Step 3: Correlation Analysis ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Examine relationships between all business metrics. .. code-block:: bash curl -X POST https://rmcp-server-394229601724.us-central1.run.app/mcp \ -H "Content-Type: application/json" \ -H "MCP-Protocol-Version: 2025-06-18" \ -H "MCP-Session-Id: YOUR_SESSION_ID" \ -d '{ "jsonrpc": "2.0", "id": 3, "method": "tools/call", "params": { "name": "correlation_analysis", "arguments": { "data": { "sales": [15000, 18000, 12000, 22000, 16000, 20000, 14000, 25000, 13000, 19000], "marketing_spend": [2000, 2500, 1500, 3000, 2200, 2800, 1800, 3500, 1600, 2600], "customer_satisfaction": [8.5, 8.8, 7.2, 9.1, 8.0, 8.9, 7.8, 9.3, 7.5, 8.7], "website_visits": [1200, 1500, 900, 1800, 1300, 1700, 1100, 2000, 950, 1600] }, "method": "pearson" } } }' **Actual Response (Verified):** .. code-block:: json { "jsonrpc": "2.0", "id": 3, "result": { "content": [ { "type": "text", "text": "## Correlation Matrix\n\n|Variable | sales| marketing_spend| customer_satisfaction| website_visits|\n|:---------------------|-----:|---------------:|---------------------:|--------------:|\n|sales | 1.000| 0.997| 0.930| 0.992|\n|marketing_spend | 0.997| 1.000| 0.940| 0.996|\n|customer_satisfaction | 0.930| 0.940| 1.000| 0.951|\n|website_visits | 0.992| 0.996| 0.951| 1.000|" } ], "structuredContent": { "type": "json", "json": { "correlation_matrix": { "sales": { "marketing_spend": 0.9973, "customer_satisfaction": 0.93, "website_visits": 0.9918 } }, "significance_tests": { "sales_marketing_spend": { "correlation": 0.9973, "p_value": 2.408e-10 }, "sales_customer_satisfaction": { "correlation": 0.93, "p_value": 0.0001 } } } } } } **Key Insights:** - **Sales ↔ Marketing**: r = 0.997 (p < 0.001) - Very strong correlation - **Sales ↔ Website Visits**: r = 0.992 (p < 0.001) - Very strong correlation - **Sales ↔ Customer Satisfaction**: r = 0.930 (p < 0.001) - Strong correlation - **All relationships**: Statistically significant with p < 0.001 Python Client Implementation ---------------------------- Complete working Python client based on the validated examples: .. code-block:: python import requests import json class RMCPBusinessAnalytics: def __init__(self): self.base_url = "https://rmcp-server-394229601724.us-central1.run.app" self.session = requests.Session() self.session_id = None def initialize(self): """Initialize MCP session""" response = self.session.post( f"{self.base_url}/mcp", headers={ "Content-Type": "application/json", "MCP-Protocol-Version": "2025-06-18" }, json={ "jsonrpc": "2.0", "id": 1, "method": "initialize", "params": { "protocolVersion": "2025-06-18", "capabilities": {}, "clientInfo": { "name": "business-analytics", "version": "1.0" } } } ) if response.ok: self.session_id = response.headers.get("Mcp-Session-Id") return response.json() else: raise Exception(f"Failed to initialize: {response.text}") def analyze_sales_performance(self, sales_data): """Analyze sales performance using linear regression""" if not self.session_id: self.initialize() response = self.session.post( f"{self.base_url}/mcp", headers={ "Content-Type": "application/json", "MCP-Protocol-Version": "2025-06-18", "MCP-Session-Id": self.session_id }, json={ "jsonrpc": "2.0", "id": 2, "method": "tools/call", "params": { "name": "linear_model", "arguments": { "formula": "sales ~ marketing_spend + customer_satisfaction", "data": sales_data, "family": "gaussian" } } } ) return response.json() def analyze_correlations(self, business_data): """Analyze correlations between business metrics""" if not self.session_id: self.initialize() response = self.session.post( f"{self.base_url}/mcp", headers={ "Content-Type": "application/json", "MCP-Protocol-Version": "2025-06-18", "MCP-Session-Id": self.session_id }, json={ "jsonrpc": "2.0", "id": 3, "method": "tools/call", "params": { "name": "correlation_analysis", "arguments": { "data": business_data, "method": "pearson" } } } ) return response.json() # Usage Example if __name__ == "__main__": client = RMCPBusinessAnalytics() # Sample business data sales_data = { "sales": [15000, 18000, 12000, 22000, 16000, 20000, 14000, 25000, 13000, 19000], "marketing_spend": [2000, 2500, 1500, 3000, 2200, 2800, 1800, 3500, 1600, 2600], "customer_satisfaction": [8.5, 8.8, 7.2, 9.1, 8.0, 8.9, 7.8, 9.3, 7.5, 8.7] } business_data = { **sales_data, "website_visits": [1200, 1500, 900, 1800, 1300, 1700, 1100, 2000, 950, 1600] } # Perform analyses regression_results = client.analyze_sales_performance(sales_data) correlation_results = client.analyze_correlations(business_data) print("Regression Analysis:") print(json.dumps(regression_results, indent=2)) print("\nCorrelation Analysis:") print(json.dumps(correlation_results, indent=2)) JavaScript Client Implementation ------------------------------- .. code-block:: javascript class RMCPBusinessAnalytics { constructor() { this.baseUrl = 'https://rmcp-server-394229601724.us-central1.run.app'; this.sessionId = null; } async initialize() { const response = await fetch(`${this.baseUrl}/mcp`, { method: 'POST', headers: { 'Content-Type': 'application/json', 'MCP-Protocol-Version': '2025-06-18' }, body: JSON.stringify({ jsonrpc: '2.0', id: 1, method: 'initialize', params: { protocolVersion: '2025-06-18', capabilities: {}, clientInfo: { name: 'business-analytics-js', version: '1.0' } } }) }); if (response.ok) { this.sessionId = response.headers.get('Mcp-Session-Id'); return await response.json(); } else { throw new Error(`Failed to initialize: ${await response.text()}`); } } async analyzeSalesPerformance(salesData) { if (!this.sessionId) { await this.initialize(); } const response = await fetch(`${this.baseUrl}/mcp`, { method: 'POST', headers: { 'Content-Type': 'application/json', 'MCP-Protocol-Version': '2025-06-18', 'MCP-Session-Id': this.sessionId }, body: JSON.stringify({ jsonrpc: '2.0', id: 2, method: 'tools/call', params: { name: 'linear_model', arguments: { formula: 'sales ~ marketing_spend + customer_satisfaction', data: salesData, family: 'gaussian' } } }) }); return await response.json(); } async analyzeCorrelations(businessData) { if (!this.sessionId) { await this.initialize(); } const response = await fetch(`${this.baseUrl}/mcp`, { method: 'POST', headers: { 'Content-Type': 'application/json', 'MCP-Protocol-Version': '2025-06-18', 'MCP-Session-Id': this.sessionId }, body: JSON.stringify({ jsonrpc: '2.0', id: 3, method: 'tools/call', params: { name: 'correlation_analysis', arguments: { data: businessData, method: 'pearson' } } }) }); return await response.json(); } } // Usage Example async function runBusinessAnalysis() { const client = new RMCPBusinessAnalytics(); const salesData = { sales: [15000, 18000, 12000, 22000, 16000, 20000, 14000, 25000, 13000, 19000], marketing_spend: [2000, 2500, 1500, 3000, 2200, 2800, 1800, 3500, 1600, 2600], customer_satisfaction: [8.5, 8.8, 7.2, 9.1, 8.0, 8.9, 7.8, 9.3, 7.5, 8.7] }; const businessData = { ...salesData, website_visits: [1200, 1500, 900, 1800, 1300, 1700, 1100, 2000, 950, 1600] }; try { const regressionResults = await client.analyzeSalesPerformance(salesData); const correlationResults = await client.analyzeCorrelations(businessData); console.log('Regression Analysis:', regressionResults); console.log('Correlation Analysis:', correlationResults); } catch (error) { console.error('Analysis failed:', error); } } Common Patterns and Best Practices ---------------------------------- Session Management ~~~~~~~~~~~~~~~~~~ ✅ **Always initialize sessions first** ✅ **Extract session ID from response headers** ✅ **Include session ID in all subsequent requests** ✅ **Handle session expiration gracefully** .. code-block:: python def ensure_session(self): if not self.session_id: self.initialize() # Add retry logic for expired sessions Data Format Guidelines ~~~~~~~~~~~~~~~~~~~~~~ ✅ **Use JSON objects for structured data** ✅ **Ensure consistent data types within arrays** ✅ **Include proper variable names** ✅ **Validate data before sending** .. code-block:: json // Good: Consistent data structure { "sales": [15000, 18000, 12000], "marketing": [2000, 2500, 1500] } // Bad: Inconsistent types { "sales": [15000, "18000", null], "marketing": [2000, 2500, 1500] } Error Handling ~~~~~~~~~~~~~~ ✅ **Check HTTP response status** ✅ **Parse JSON-RPC error responses** ✅ **Implement retry logic for network issues** ✅ **Log errors with context** .. code-block:: python def handle_response(self, response): if not response.ok: raise Exception(f"HTTP {response.status_code}: {response.text}") data = response.json() if 'error' in data: error = data['error'] raise Exception(f"RMCP Error {error['code']}: {error['message']}") return data['result'] Performance Tips ~~~~~~~~~~~~~~~~ ✅ **Reuse sessions for multiple operations** ✅ **Batch related analyses when possible** ✅ **Use appropriate timeout values** ✅ **Monitor response times** Validation Results ------------------ **Test Date**: October 21, 2025 **Server Version**: RMCP MCP Server v0.7.0 **Test Environment**: Production deployment on Google Cloud Run ✅ **Session Initialization**: Working ✅ **Linear Regression**: Working (R² = 0.995) ✅ **Correlation Analysis**: Working (all correlations > 0.9) ✅ **JSON Response Format**: Valid ✅ **Error Handling**: Proper JSON-RPC errors ✅ **Session Management**: Headers working correctly **Performance**: - **Session Init**: ~200ms - **Linear Regression**: ~3s (10 observations) - **Correlation Analysis**: ~1.5s (4 variables) - **Total Analysis Time**: ~5s for complete business analysis Next Steps ---------- 1. **Explore More Tools**: Use ``/docs`` to see all 44 available statistical tools 2. **Scale Analysis**: Try with larger datasets (up to 10,000 observations) 3. **Advanced Models**: Experiment with time series, machine learning tools 4. **Integration**: Build these examples into your applications 5. **Visualization**: Use plotting tools for data visualization 🔗 **Related Resources**: - **Interactive API Docs**: https://rmcp-server-394229601724.us-central1.run.app/docs - **Complete Tool Reference**: :doc:`api-reference` - **Python Package Equivalent**: :doc:`../shared/examples` - **Troubleshooting**: :doc:`../shared/troubleshooting`