πŸ‡ΊπŸ‡Έ English πŸ‡ͺπŸ‡Έ EspaΓ±ol

πŸš€ PeopleWorks GPT MCP Server

Bridging AI Assistants with Enterprise Databases

"Making PeopleWorks GPT capabilities accessible to any MCP-compatible AI assistant"

🎯 What is This About?

Imagine you're chatting with Claude Desktop, and you want to ask: "Show me our top 10 properties by revenue this month". Without the MCP Server, Claude would politely say it can't access your database. But with the PeopleWorks GPT MCP Server, Claude can authenticate, connect to your SQL Server database, generate the optimal SQL query using AI, execute it, and return beautiful results - all in a single conversational exchange.

πŸŽ‰ The Revolutionary Concept

The Model Context Protocol (MCP) is Anthropic's groundbreaking standard that allows AI assistants like Claude to securely connect to external data sources and tools. Think of it as a universal adapter that lets any AI assistant talk to any data source or service - databases, APIs, file systems, and more.

What is MCP? A Simple Analogy
πŸ”Œ Before MCPLike having different chargers for every device - iPhone uses Lightning, Android uses USB-C, laptops use proprietary chargers Fragmented
⚑ With MCPLike USB-C becoming universal - one protocol that works with every AI assistant and every data source Unified
🌐 The ResultAny MCP-compatible AI (Claude, ChatGPT, custom agents) can instantly connect to any MCP server (databases, CRMs, ERPs, APIs) Interoperable

πŸ’‘ Why PeopleWorks Built This

🎯

Leverage Existing Infrastructure

PeopleWorks already has a powerful DatabaseGPT engine that converts natural language to SQL. The MCP Server exposes this capability to Claude Desktop and other MCP clients without rewriting a single line of core logic.

πŸ”

Enterprise-Grade Security

Multi-user authentication with API keys, JWT session tokens, granular database permissions, and complete audit logging - all the security features businesses demand.

🌍

Universal Database Support

Works with SQL Server, PostgreSQL, MySQL, Oracle, and more. The abstraction layer handles dialect differences automatically.

🧠

Domain Knowledge Injection

Database hints and custom prompts teach the AI about your business domain, resulting in more accurate and context-aware queries.

πŸ“Š

Beyond Simple Queries

Not just SELECT statements - the server supports analytics templates, drill-downs, comparisons, data quality audits, and complex business intelligence queries.

πŸš€

Future-Proof Integration

Once built, any MCP-compatible client (Claude Desktop, future versions of ChatGPT, custom AI agents, STRPlatform) can use it immediately.

πŸ—οΈ Architecture Overview

graph TB subgraph "MCP Clients" Claude[Claude Desktop] STR[STRPlatform] Custom[Custom AI Agents] end subgraph "PeopleWorks MCP Server" Auth[Authentication Tool] Conn[Connection Management] Query[Query Execution Tool] Schema[Schema Management] Hints[Hints Management] Auth --> Session[Session Manager
JWT Tokens] Query --> QES[Query Execution Service] QES --> DBGPT[DatabaseGPT Engine] Query --> Audit[Audit Logger] end subgraph "Data Layer" DBGPT --> Provider[AI Provider
OpenAI/Azure/Anthropic] DBGPT --> DB[(SQL Server
PostgreSQL
MySQL
Oracle)] end Claude -.->|MCP Protocol
STDIO/HTTP| Auth STR -.->|MCP Protocol
HTTP| Auth Custom -.->|MCP Protocol
HTTP| Auth style Claude fill:#2563eb,stroke:#1e40af,color:#fff style STR fill:#7c3aed,stroke:#6d28d9,color:#fff style Custom fill:#06b6d4,stroke:#0891b2,color:#fff style DBGPT fill:#10b981,stroke:#059669,color:#fff style Provider fill:#f59e0b,stroke:#d97706,color:#fff

Architectural Layers

πŸ”Œ Transport Layer
STDIO Transport (Development) HTTP Transport (Production) Stateless Sessions CORS Support
πŸ” Security Layer
API Key Authentication JWT Session Tokens User-Level Permissions Query Audit Logging
πŸ› οΈ MCP Tools Layer
Authentication Tool Connection Management Query Execution Schema Management Hints Management Suggested Questions Explain Query
🎨 Prompts & Resources Layer
Analytics Templates Data Exploration Drill-Down Patterns Comparison Queries Data Quality Audits Schema Resources
🧠 DatabaseGPT Core
Natural Language Parser SQL Generator Multi-Database Support Hints Integration Query Optimizer

βš™οΈ MCP Capabilities: Tools, Prompts & Resources

πŸ“š Understanding MCP Capabilities

MCP servers expose three types of capabilities: Tools (executable actions), Prompts (reusable templates), and Resources (structured data). Let's explore each in detail.

πŸ”§ Tools (Executable Actions)

Tool Purpose Key Parameters Returns
AuthenticateAsync User authentication and session creation username, apiKey JWT session token (60 min expiry)
ListConnectionsAsync Discover available databases sessionToken List of accessible database connections
ExecuteQueryAsync Execute natural language query sessionToken, connectionId, query, includeHints Generated SQL + result set + execution metrics
GetSchemaAsync Retrieve database schema sessionToken, connectionId, specificTables Tables, columns, types, constraints in markdown
ListHintsAsync View active database hints sessionToken, connectionId Business rules, custom prompts, context
UpdateHintsAsync Modify database hints sessionToken, connectionId, hintsContent Success confirmation
GetSuggestedQuestionsAsync AI-generated suggested queries sessionToken, connectionId, category List of relevant questions user can ask
ExplainQueryAsync Explain SQL generation process sessionToken, connectionId, query Step-by-step explanation of SQL generation

πŸ’‘ Prompts (Query Templates)

Reusable Business Intelligence Patterns

Prompts are pre-configured templates that guide the AI in generating complex analytical queries. They encode best practices for common BI scenarios.

Prompt Template Use Case Example
Analytics Query Business metrics with aggregations "Total revenue by property type for last quarter with rankings"
Data Exploration Statistical discovery patterns "Find unusual patterns in booking durations by season"
Drill-Down Hierarchical analysis "Break down total revenue by region β†’ city β†’ property"
A/B Comparison Side-by-side metrics "Compare Q1 2024 vs Q1 2025: revenue, bookings, occupancy"
Data Quality Audit and validation "Check Properties table for NULLs, duplicates, outliers in price"

πŸ“š Resources (Structured Data)

πŸ—‚οΈ

Schema Information

Complete database schema: tables, columns, data types, relationships, indexes. Returned in structured JSON for efficient parsing.

πŸ“‹

Table Details

CREATE TABLE scripts with full DDL, constraints, defaults, and computed columns for deep schema understanding.

πŸ’Ύ

Sample Data

First 5 rows from any table to understand data patterns, formats, and actual values - crucial for context-aware queries.

πŸ“–

Database Hints

Business rules, custom prompts, multi-tenant context, and domain knowledge that enhance query generation accuracy.

πŸ’» Code Deep Dive

Authentication Tool Implementation

using Infrastructure.Persistence.Contexts;
using Microsoft.EntityFrameworkCore;
using ModelContextProtocol.Server;
using PeopleworksGPT.MCP.Server.Services;
using System.ComponentModel;
using System.Text.Json;

namespace PeopleworksGPT.MCP.Server.Tools
{
    [McpServerToolType]
    public sealed class AuthenticationTool
    {
        private readonly ApplicationDbContext _context;
        private readonly SessionManager _sessionManager;

        public AuthenticationTool(ApplicationDbContext context, SessionManager sessionManager)
        {
            _context = context;
            _sessionManager = sessionManager;
        }

        [McpServerTool(Name = "AuthenticateAsync")]
        [Description("Authenticate user and receive session token for subsequent requests")]
        public async Task<string> AuthenticateAsync(
            [Description("Username")] string username,
            [Description("API key for authentication")] string apiKey)
        {
            try
            {
                // Find user by username and API key
                var user = await _context.ApplicationUsers
                    .FirstOrDefaultAsync(u => u.UserName == username 
                        && u.ApiKey == apiKey 
                        && u.IsActive 
                        && !u.Deleted);

                if (user == null)
                {
                    return JsonSerializer.Serialize(new
                    {
                        success = false,
                        error = "Invalid credentials"
                    });
                }

                // Update last login timestamp
                user.UpdatedDate = DateTime.Now;
                await _context.SaveChangesAsync();

                // Generate JWT token with 60-minute expiration
                var sessionToken = _sessionManager.GenerateJwtToken(user.Id, user.UserName!);

                return JsonSerializer.Serialize(new
                {
                    success = true,
                    session_token = sessionToken,
                    user_id = user.Id,
                    username = user.UserName,
                    expires_at = DateTime.UtcNow.AddMinutes(60).ToString("O")
                });
            }
            catch (Exception ex)
            {
                return JsonSerializer.Serialize(new
                {
                    success = false,
                    error = $"Authentication failed: {ex.Message}"
                });
            }
        }
    }
}
πŸ” Key Implementation Details
  • [McpServerToolType] - Marks the class as containing MCP tools
  • [McpServerTool] - Exposes the method as an MCP tool
  • [Description] - Provides AI-readable documentation
  • Returns JSON for structured parsing by AI clients
  • Secure validation: active users only, soft-delete aware

Query Execution Tool - The Heart of the System

[McpServerTool(Name = "ExecuteQueryAsync")]
[Description("Execute a natural language query against a specific database connection")]
public async Task<string> ExecuteQueryAsync(
    [Description("Session token from authenticate()")] string sessionToken,
    [Description("Database connection ID from list_connections()")] long connectionId,
    [Description("Natural language query")] string query,
    [Description("Include database hints (default: true)")] bool includeHints = true,
    [Description("Additional context for filtering")] string? additionalContext = null,
    [Description("Security filter for multi-tenant isolation (JSON)")] string? securityFilter = null)
{
    var stopwatch = Stopwatch.StartNew();
    
    try
    {
        // 1. Validate JWT session token
        var (isValid, userId, username) = _sessionManager.ValidateToken(sessionToken);
        if (!isValid)
        {
            return JsonSerializer.Serialize(new { 
                success = false, 
                error = "Invalid or expired session token" 
            });
        }

        // 2. Verify user has access to this connection
        var connection = await _context.DatabaseConnectionSettings
            .FirstOrDefaultAsync(c => 
                c.Id == connectionId && 
                c.AvailableForUsers.Any(u => u.Id == userId) && 
                !c.Deleted && 
                !c.Disabled);

        if (connection == null)
        {
            return JsonSerializer.Serialize(new { 
                success = false, 
                error = "Connection not found or access denied" 
            });
        }

        // 3. Execute query through DatabaseGPT engine
        var (sql, results) = await _queryExecutionService.ExecuteNaturalLanguageQueryAsync(
            connection,
            query,
            includeHints,
            additionalContext,
            securityFilter);

        stopwatch.Stop();

        // 4. Log to audit trail
        await _auditLogger.LogQueryAsync(
            userId,
            connectionId,
            query,
            sql,
            (int)stopwatch.ElapsedMilliseconds,
            results.Count,
            success: true);

        // 5. Return structured results
        return JsonSerializer.Serialize(new
        {
            success = true,
            query_id = Guid.NewGuid().ToString(),
            original_query = query,
            generated_sql = sql,
            results,
            row_count = results.Count,
            execution_time_ms = stopwatch.ElapsedMilliseconds,
            connection_name = connection.DbName
        });
    }
    catch (Exception ex)
    {
        stopwatch.Stop();
        
        // Log failure to audit
        await _auditLogger.LogQueryAsync(
            userId, connectionId, query, null,
            (int)stopwatch.ElapsedMilliseconds, 0, 
            success: false, errorMessage: ex.Message);

        return JsonSerializer.Serialize(new
        {
            success = false,
            error = $"Query execution failed: {ex.Message}"
        });
    }
}

Session Manager - JWT Token Handling

public class SessionManager
{
    private readonly IConfiguration _configuration;

    public string GenerateJwtToken(long userId, string username)
    {
        var secretKey = _configuration["JWT:SecretKey"] 
            ?? throw new InvalidOperationException("JWT SecretKey not configured");
        
        var securityKey = new SymmetricSecurityKey(
            Encoding.UTF8.GetBytes(secretKey));
        var credentials = new SigningCredentials(
            securityKey, SecurityAlgorithms.HmacSha256);

        var claims = new[]
        {
            new Claim(ClaimTypes.NameIdentifier, userId.ToString()),
            new Claim(ClaimTypes.Name, username),
            new Claim(JwtRegisteredClaimNames.Jti, Guid.NewGuid().ToString())
        };

        var token = new JwtSecurityToken(
            issuer: _configuration["JWT:Issuer"],
            audience: _configuration["JWT:Audience"],
            claims: claims,
            expires: DateTime.UtcNow.AddMinutes(60),
            signingCredentials: credentials
        );

        return new JwtSecurityTokenHandler().WriteToken(token);
    }

    public (bool IsValid, long UserId, string Username) ValidateToken(string token)
    {
        try
        {
            var tokenHandler = new JwtSecurityTokenHandler();
            var validationParameters = new TokenValidationParameters
            {
                ValidateIssuer = true,
                ValidateAudience = true,
                ValidateLifetime = true,
                ValidateIssuerSigningKey = true,
                ValidIssuer = _configuration["JWT:Issuer"],
                ValidAudience = _configuration["JWT:Audience"],
                IssuerSigningKey = new SymmetricSecurityKey(
                    Encoding.UTF8.GetBytes(_configuration["JWT:SecretKey"]!))
            };

            var principal = tokenHandler.ValidateToken(token, validationParameters, out _);
            var userId = long.Parse(principal.FindFirst(ClaimTypes.NameIdentifier)!.Value);
            var username = principal.FindFirst(ClaimTypes.Name)!.Value;

            return (true, userId, username);
        }
        catch
        {
            return (false, 0, string.Empty);
        }
    }
}

πŸš€ Deployment Scenarios

Feature STDIO Transport
(Development)
HTTP Transport
(Production)
How it Works Claude executes dotnet run each time Server runs permanently, Claude connects via HTTP
Setup Complexity βœ“ Copy config file, restart Claude βœ— Publish, deploy, configure IIS/Docker
Source Code Required βœ— Yes, on client machine βœ“ No, compiled binary only
Multi-Client Support βœ— Single user only βœ“ Unlimited concurrent users
Code Changes βœ“ Immediate effect βœ— Requires redeploy
Remote Access βœ— Local only βœ“ Cloud, corporate network, anywhere
Best For Developers, quick testing, local experimentation Production, teams, STRPlatform integration, enterprise

STDIO Configuration Example

{
  "mcpServers": {
    "peopleworksgpt": {
      "command": "dotnet",
      "args": [
        "run",
        "--project",
        "C:\\Proyecto\\AI\\PeopleworksGPT\\PeopleworksGPT.MCP.Server\\PeopleworksGPT.MCP.Server.csproj"
      ],
      "env": {
        "ASPNETCORE_ENVIRONMENT": "Development"
      }
    }
  }
}
πŸ“ Config File Location

Windows: %APPDATA%\Claude\claude_desktop_config.json

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

HTTP Configuration Example

{
  "mcpServers": {
    "peopleworksgpt": {
      "url": "https://mcp.peopleworks.com/mcp",
      "transport": "http",
      "headers": {
        "MCP-Protocol-Version": "2025-06-18"
      }
    }
  }
}

IIS Deployment Script

# Publish the application
dotnet publish -c Release -o C:\inetpub\PeopleworksGPT-MCP

# Create IIS Application Pool
New-WebAppPool -Name "PeopleworksGPT-MCP" -Force
Set-ItemProperty IIS:\AppPools\PeopleworksGPT-MCP `
    -Name managedRuntimeVersion -Value ""

# Create IIS Website
New-WebSite -Name "PeopleworksGPT-MCP" `
    -Port 5000 `
    -PhysicalPath "C:\inetpub\PeopleworksGPT-MCP" `
    -ApplicationPool "PeopleworksGPT-MCP" `
    -Force

# Configure web.config for InProcess hosting
$webConfig = @"
<?xml version="1.0" encoding="utf-8"?>
<configuration>
  <system.webServer>
    <handlers>
      <add name="aspNetCore" path="*" verb="*" 
           modules="AspNetCoreModuleV2" 
           resourceType="Unspecified" />
    </handlers>
    <aspNetCore processPath="dotnet" 
                arguments=".\PeopleworksGPT.MCP.Server.dll" 
                stdoutLogEnabled="true" 
                stdoutLogFile=".\logs\stdout" 
                hostingModel="InProcess" />
  </system.webServer>
</configuration>
"@

Set-Content -Path "C:\inetpub\PeopleworksGPT-MCP\web.config" -Value $webConfig

Write-Host "Deployment complete! Server available at http://localhost:5000" -ForegroundColor Green

πŸ”„ Typical User Workflow

Authentication

User provides username and API key. Server validates credentials and generates a JWT session token valid for 60 minutes.

User: "Authenticate me with username 'admin' and apiKey 'pk_admin_1'"
Claude: [Calls AuthenticateAsync tool]
Response: βœ… Authenticated! Session token: eyJ0eXAiOiJKV1Q...

Discover Connections

User requests available database connections. Server returns only connections the authenticated user has permission to access.

User: "Show me my database connections"
Claude: [Calls ListConnectionsAsync tool]
Response: πŸ“Š You have access to:
  1. MonetSTR-Production (SQL Server 2022)
  2. Analytics-Warehouse (PostgreSQL 15)

Explore Schema

User examines database structure. Server provides table definitions, relationships, and sample data.

User: "What tables are in MonetSTR-Production?"
Claude: [Calls GetSchemaAsync tool]
Response: πŸ“‹ Database Schema:
  β€’ Properties (15 columns)
  β€’ Bookings (22 columns)
  β€’ Customers (18 columns)
  β€’ Payments (12 columns)

Execute Natural Language Query

User asks a business question. Server converts it to optimized SQL, executes it, and returns formatted results.

User: "Show me top 10 properties by revenue for November 2024"
Claude: [Calls ExecuteQueryAsync tool]
Generated SQL:
SELECT TOP 10 
    PropertyName,
    SUM(TotalAmount) AS TotalRevenue
FROM Bookings
WHERE BookingDate >= '2024-11-01' AND BookingDate < '2024-12-01'
GROUP BY PropertyName
ORDER BY TotalRevenue DESC

Results: 
  1. Beach Villa Sunset - $125,450
  2. Downtown Loft 42 - $98,320
  ...

Follow-up & Refinement

User asks follow-up questions. Claude maintains context and refines queries based on conversation history.

User: "Now show me the same for December"
Claude: [Modifies query, calls ExecuteQueryAsync]
User: "What about year-over-year comparison?"
Claude: [Uses Comparison Template prompt, generates complex SQL with CTEs]

🎯 Advanced Features

πŸ’‘

Database Hints

Inject domain knowledge: "PropertyStatus 1=Active, 2=Inactive" or "Revenue is calculated as TotalAmount - Discounts". The AI uses these hints to generate more accurate queries.

🎨

Custom Prompts

Define business-specific templates: "For hospitality industry, always calculate occupancy rate as (booked_nights / available_nights) * 100"

πŸ”

Multi-Tenant Security

Automatic filtering: "ClientId = @CurrentUserClientId" injected into all queries to ensure data isolation in multi-tenant systems.

πŸ“Š

Audit Trail

Every query logged: user, timestamp, natural language input, generated SQL, execution time, rows returned, success/failure.

πŸ”

Query Explanation

Ask "Explain how you generated that SQL" - get step-by-step breakdown of AI's reasoning process.

πŸ’¬

Suggested Questions

AI analyzes your schema and suggests relevant questions: "What's the average booking duration?", "Which properties have the highest occupancy?"

🎬 Real-World Use Cases

Scenario Traditional Approach With MCP Server
Executive Dashboard Hire BI team β†’ Build reports β†’ Wait weeks β†’ Limited flexibility Ask Claude: "Show me KPIs for all properties this quarter" β†’ Instant results β†’ Drill down conversationally
Data Quality Audit Write SQL scripts β†’ Manual testing β†’ Document findings Ask: "Check all tables for NULL values, duplicates, and outliers" β†’ Comprehensive report in seconds
Customer Support Support agent β†’ Escalate to dev team β†’ Wait for query β†’ Manual response Support asks Claude: "Find bookings for customer John Doe in last 6 months" β†’ Immediate answer
Business Analysis Define requirements β†’ Request report β†’ Wait for developer β†’ Iterate Analyst chats with Claude: "Compare Q1 vs Q2 revenue by region with variance analysis" β†’ Done
Data Migration Validation Write validation scripts β†’ Run manually β†’ Compare results β†’ Report Ask: "Compare row counts and sum totals between source and target for all tables" β†’ Instant validation

πŸ”¬ Technical Architecture Deep Dive

Program.cs - Server Initialization

var builder = WebApplication.CreateBuilder(args);

// Configure Serilog for structured logging
builder.Host.UseSerilog();

// Add database context with SQL Server
builder.Services.AddDbContext<ApplicationDbContext>(options =>
{
    var connectionString = builder.Configuration.GetConnectionString("PeopleworksGPT");
    options.UseSqlServer(connectionString);
});

// Add Identity for user management
builder.Services.AddIdentity<ApplicationUser, IdentityRole<long>>()
    .AddEntityFrameworkStores<ApplicationDbContext>();

// Add CORS for HTTP transport
builder.Services.AddCors(options =>
{
    options.AddPolicy("MCPClients", policy =>
    {
        policy.AllowAnyOrigin()
              .AllowAnyHeader()
              .AllowAnyMethod();
    });
});

// Add PeopleWorks services (reuse existing infrastructure!)
builder.Services.AddScoped<ISettingService, SettingService>();
builder.Services.AddScoped<IPromptProvider, PromptProvider>();
builder.Services.AddScoped<IOpenAiCompatiblePwDatabaseGptClient, OpenAiCompatiblePwDatabaseGptClient>();

// Add MCP-specific services
builder.Services.AddScoped<SessionManager>();
builder.Services.AddScoped<AuditLogger>();
builder.Services.AddScoped<QueryExecutionService>();

// Add MCP Server with automatic tool discovery
builder.Services.AddMcpServer(options =>
{
    options.ServerInfo = new Implementation
    {
        Name = "PeopleWorksGPT",
        Version = "1.0.0"
    };
})
.WithHttpTransport(httpOptions =>
{
    httpOptions.Stateless = true; // No session state required
})
.WithToolsFromAssembly(); // Auto-discover all [McpServerTool] methods

var app = builder.Build();

// Enable CORS
app.UseCors("MCPClients");

// Map MCP endpoints
app.MapMcp();

await app.RunAsync();
🎯 Architectural Decisions
  • Stateless HTTP: No session cookies required - all state in JWT token
  • Auto-Discovery: WithToolsFromAssembly() finds all tools automatically
  • Dependency Injection: Full DI support for all services
  • Reuse Existing Code: MCP server leverages 100% of DatabaseGPT infrastructure

Multi-Database Support Architecture

graph LR NLQ[Natural Language Query] --> QES[Query Execution Service] QES --> DBGPT[DatabaseGPT Engine] DBGPT --> Provider{Database Provider} Provider -->|SQL Server| TSQL[T-SQL Generator] Provider -->|PostgreSQL| PGSQL[PostgreSQL Generator] Provider -->|MySQL| MySQL[MySQL Generator] Provider -->|Oracle| PLSQL[PL/SQL Generator] TSQL --> Execute[Execute Query] PGSQL --> Execute MySQL --> Execute PLSQL --> Execute Execute --> Results[Return Results] style DBGPT fill:#10b981,stroke:#059669,color:#fff style Provider fill:#f59e0b,stroke:#d97706,color:#fff style Results fill:#2563eb,stroke:#1e40af,color:#fff

⚑ Performance & Scalability

Metric Value Optimization
Authentication Latency < 100ms Indexed database lookups, efficient JWT generation
Query Generation Time 1-3 seconds Depends on AI provider latency (OpenAI/Azure)
Query Execution Varies by complexity Optimized SQL generation, proper indexing hints
Concurrent Users Limited by database connections Connection pooling, async/await throughout
Schema Caching Planned for v2.0 Memory cache for frequently accessed schemas
πŸ”§ Scalability Strategies
  • Horizontal Scaling: Multiple MCP server instances behind load balancer (stateless design enables this)
  • Caching Layer: Redis for schema metadata, query results, session tokens
  • Queue-Based Processing: For long-running analytical queries, use message queue pattern
  • Read Replicas: Point queries to read-only database replicas for heavy read workloads
  • CDN Distribution: Deploy MCP servers in multiple regions for global teams

πŸ”’ Security Best Practices

πŸ”‘

API Key Storage

Current: Plain text in database
Recommended: Encrypted using AES-256, key rotation policy, Azure Key Vault integration

πŸ”

JWT Security

Current: 60-minute expiration
Enhancement: Refresh tokens, token revocation list, shorter expiration with auto-refresh

🌐

Network Security

Required: HTTPS/TLS for production
Best: mTLS for client authentication, IP whitelisting, VPN access

πŸ›‘οΈ

SQL Injection Protection

Built-in: Parameterized queries only
Additional: Query validation, SQL parsing, dangerous keyword detection

πŸ“‹

Audit Logging

Current: Query audit to database
Enhanced: SIEM integration, anomaly detection, compliance reporting (GDPR, HIPAA)

⚠️

Rate Limiting

Planned: Per-user query limits
Recommendation: 100 queries/hour, burst protection, cost controls for AI API usage

⚠️ Production Checklist
  • βœ… Change JWT secret key from default
  • βœ… Encrypt API keys in database
  • βœ… Enable HTTPS with valid SSL certificate
  • βœ… Implement rate limiting
  • βœ… Set up monitoring and alerting
  • βœ… Configure database connection pooling
  • βœ… Enable query timeout limits
  • βœ… Implement backup and disaster recovery

πŸ—ΊοΈ Future Roadmap

Phase 1: Performance Optimization (Q1 2026)

  • Schema metadata caching with Redis
  • Query result caching for repeated queries
  • Async query execution for long-running reports
  • Connection pooling optimization

Phase 2: Advanced Features (Q2 2026)

  • Query builder UI for visual query construction
  • Saved queries and favorites
  • Query sharing and collaboration
  • Excel/CSV export functionality

Phase 3: AI Provider Expansion (Q3 2026)

  • Google Gemini integration
  • Local LLM support (Ollama, LM Studio)
  • Cost optimization with model routing

Phase 4: Enterprise Features (Q4 2026)

  • SSO/SAML authentication
  • Active Directory integration
  • Advanced audit and compliance reporting
  • Data masking and PII protection
  • Multi-region deployment

πŸŽ“ Key Takeaways

What We've Built

🎯 The Core Innovation: The PeopleWorks GPT MCP Server transforms how businesses interact with their data. Instead of hiring BI teams, building dashboards, or writing SQL, users can simply ask questions in natural language.

πŸ”„ The Reusability Win: By implementing the MCP protocol, we've made DatabaseGPT's powerful AI-to-SQL capabilities accessible to any MCP-compatible client - Claude Desktop today, ChatGPT tomorrow, custom agents next week.

🏒 The Enterprise Value: Multi-user authentication, granular permissions, audit trails, multi-database support, and domain knowledge injection make this a production-ready solution for real businesses.

πŸš€ The Future Path: This is just the beginning. With STRPlatform integration, advanced analytics templates, and multi-AI-provider support on the roadmap, the platform will become even more powerful.

"The Model Context Protocol is the USB-C of AI - and PeopleWorks GPT is ready to plug in."

Ready to Transform Your Data Access?

Start using the PeopleWorks GPT MCP Server today

πŸ“– Read the Documentation Learn More πŸš€ Deploy to Production πŸ’¬ Contact PeopleWorks