How to Build a Postgres MCP Server for AI Agents

You've decided to build an MCP server for Postgres. Smart move. Direct database access for agents is a security nightmare, and an MCP server gives you the control you need. But here's the thing: building an MCP server that's secure, performant, and production-ready isn't trivial.

I've seen teams build MCP servers that leak credentials, exhaust connection pools, or allow SQL injection attacks. The difference between a working prototype and a production-ready server is understanding Postgres connection management, query optimization, and security best practices.

A Postgres MCP server sits between your agents and your database. It handles connection pooling, query validation, access control, and error handling. It turns raw Postgres access into safe, governed tools that agents can use without breaking your database or exposing sensitive data.

This guide shows you how to build a production-ready Postgres MCP server from scratch. You'll learn connection management, query optimization, security patterns, and deployment strategies that actually work in production.

Table of Contents

• Why Build a Postgres MCP Server?
• Understanding Postgres Connection Management
• Architecture: MCP Server Pattern
• Step 1: Setting Up Postgres Permissions
• Step 2: Building the MCP Server
• Step 3: Creating Tool Handlers
• Step 4: Adding Security Layers
• Step 5: Deploying to Production
• Real-World Examples
• Postgres-Specific Best Practices
• Common Mistakes to Avoid
• Where Pylar Fits In
• Frequently Asked Questions

---

Why Build a Postgres MCP Server?

Direct Postgres access for agents creates problems that an MCP server solves:

Problem 1: Connection Pool Exhaustion

What happens: Each agent instance opens a Postgres connection. Multiple agents = connection exhaustion.

Example: You have 50 concurrent agent instances, each opening a Postgres connection. Your connection pool (default: 100 connections) is exhausted. Customer-facing apps can't connect.

Impact: Application downtime, customer complaints, revenue loss.

Solution: MCP server manages a single connection pool. All agents share the pool.

Problem 2: No Query Control

What happens: Agents can write any query, including dangerous ones (DROP TABLE, DELETE, etc.).

Example: An agent generates a query that deletes all test data, but accidentally targets production.

Impact: Data loss, service disruption, recovery costs.

Solution: MCP server validates queries, restricts to SELECT statements, and enforces access boundaries.

Problem 3: Security Risks

What happens: Agents can access any table, any column, any data they have permissions for.

Example: A support agent needs to look up Customer A, but with direct access, it can query all customers, employee data, financial records, and more.

Impact: Data breaches, compliance violations, security incidents.

Solution: MCP server enforces access boundaries through sandboxed views and role-based permissions.

Problem 4: No Audit Trail

What happens: You can't track which agents accessed which data, when, or why.

Example: A compliance auditor asks: "Which agents accessed customer PII in the last 30 days?" You check Postgres logs, but they don't show agent context—just raw SQL queries.

Impact: Failed compliance audits, inability to investigate security incidents, regulatory fines.

Solution: MCP server logs every query with agent context, user identity, and access patterns. Complete audit trail.

Problem 5: Performance Impact

What happens: Agents write inefficient queries that slow down your Postgres instance.

Example: An agent writes a query that scans 10 million rows without indexes. The query takes 30 seconds, locks tables, and causes timeouts across your application.

Impact: Production performance degradation, customer-facing slowdowns, increased Postgres costs.

Solution: MCP server routes queries to read replicas, optimizes queries, and enforces query timeouts.

---

Understanding Postgres Connection Management

Before building an MCP server, you need to understand how Postgres connections work.

Connection Pooling

Postgres has a limited number of connections (default: 100). Each connection consumes memory and resources. Connection pooling reuses connections instead of creating new ones for each query.

Why it matters: Without pooling, agents exhaust your connection limit. With pooling, one pool handles unlimited agent queries.

Read Replicas

Read replicas are copies of your production Postgres database. They handle read queries without impacting production.

Why it matters: Agent queries don't impact production. Production stays fast for customer-facing services.

Roles and Permissions

Postgres uses roles to manage permissions. Each role has specific permissions on tables, views, and functions.

Why it matters: Agents should use roles with limited permissions. Never give agents superuser access.

Query Timeouts

Postgres queries can run indefinitely. Query timeouts prevent runaway queries from locking tables.

Why it matters: Without timeouts, one slow query can lock your entire database.

---

Architecture: MCP Server Pattern

The Postgres MCP server uses this architecture:

Agent → MCP Tool → Query Handler → Connection Pool → Postgres (Read Replica)

Components

1. MCP Server: Handles MCP protocol, tool definitions, and request routing.

2. Query Handler: Validates queries, enforces access boundaries, and executes queries safely.

3. Connection Pool: Manages Postgres connections efficiently.

4. Postgres Database: Read replica or production (with proper isolation).

Data Flow

1. Agent calls MCP tool with parameters
2. MCP server validates parameters
3. Query handler builds parameterized query
4. Connection pool provides connection
5. Query executes on Postgres
6. Results return to agent
7. Connection returns to pool

---

Step 1: Setting Up Postgres Permissions

Before building the MCP server, set up Postgres permissions correctly.

Create Agent Role

Create a dedicated role for agents:

-- Create agent role
CREATE ROLE agent_role WITH LOGIN PASSWORD 'secure-password';

-- Grant connection permission
GRANT CONNECT ON DATABASE your_database TO agent_role;

-- Grant usage on schema
GRANT USAGE ON SCHEMA public TO agent_role;

Create Sandboxed Views

Create views that agents can access:

-- Customer Support View (Sandboxed)
CREATE VIEW customer_support_view AS
SELECT 
  id,
  name,
  email,
  plan_name,
  signup_date,
  subscription_status,
  last_login_date,
  active_users_30d,
  open_tickets_count
FROM customers
WHERE is_active = true
  AND signup_date >= CURRENT_DATE - INTERVAL '2 years';

-- Grant access to view (not underlying table)
GRANT SELECT ON customer_support_view TO agent_role;

Set Up Read Replica (Optional but Recommended)

For production, use a read replica:

AWS RDS:

aws rds create-db-instance-read-replica \
  --db-instance-identifier agent-replica \
  --source-db-instance-identifier production-db \
  --db-instance-class db.r5.large

Self-Managed:

-- On production (primary)
ALTER SYSTEM SET wal_level = replica;
ALTER SYSTEM SET max_wal_senders = 3;
ALTER SYSTEM SET max_replication_slots = 3;
SELECT pg_reload_conf();

-- Create replication user
CREATE USER replicator WITH REPLICATION PASSWORD 'password';

Configure Connection Limits

Set connection limits for the agent role:

-- Limit agent role connections
ALTER ROLE agent_role WITH CONNECTION LIMIT 10;

Why this matters: Prevents a single agent from exhausting all connections.

---

Step 2: Building the MCP Server

Now let's build the MCP server. We'll use Node.js and the MCP SDK.

Initialize Project

# Create project directory
mkdir postgres-mcp-server
cd postgres-mcp-server

# Initialize Node.js project
npm init -y

# Install dependencies
npm install @modelcontextprotocol/sdk pg dotenv
npm install -D typescript @types/node @types/pg tsx

Create Project Structure

postgres-mcp-server/
├── src/
│   ├── server.ts          # MCP server implementation
│   ├── database.ts        # Postgres connection pool
│   ├── tools.ts           # Tool definitions
│   └── handlers.ts        # Tool handlers
├── .env                   # Environment variables
├── tsconfig.json          # TypeScript config
└── package.json

Set Up TypeScript

// tsconfig.json
{
  "compilerOptions": {
    "target": "ES2022",
    "module": "ESNext",
    "moduleResolution": "node",
    "outDir": "./dist",
    "rootDir": "./src",
    "strict": true,
    "esModuleInterop": true,
    "skipLibCheck": true
  }
}

Create Environment Variables

# .env
POSTGRES_HOST=your-postgres-host
POSTGRES_PORT=5432
POSTGRES_DATABASE=your_database
POSTGRES_USER=agent_role
POSTGRES_PASSWORD=secure-password
POSTGRES_SSL=true
POSTGRES_MAX_CONNECTIONS=20

Create Connection Pool

// src/database.ts
import { Pool, PoolConfig } from 'pg'
import dotenv from 'dotenv'

dotenv.config()

const poolConfig: PoolConfig = {
  host: process.env.POSTGRES_HOST,
  port: parseInt(process.env.POSTGRES_PORT || '5432'),
  database: process.env.POSTGRES_DATABASE,
  user: process.env.POSTGRES_USER,
  password: process.env.POSTGRES_PASSWORD,
  ssl: process.env.POSTGRES_SSL === 'true' ? { rejectUnauthorized: false } : false,
  max: parseInt(process.env.POSTGRES_MAX_CONNECTIONS || '20'),
  idleTimeoutMillis: 30000,
  connectionTimeoutMillis: 2000,
}

export const pool = new Pool(poolConfig)

// Test connection
pool.on('connect', () => {
  console.log('Postgres connection established')
})

pool.on('error', (err) => {
  console.error('Unexpected Postgres pool error:', err)
})

// Graceful shutdown
process.on('SIGINT', async () => {
  await pool.end()
  process.exit(0)
})

Create MCP Server

// src/server.ts
import { Server } from '@modelcontextprotocol/sdk/server/index.js'
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'
import {
  CallToolRequestSchema,
  ListToolsRequestSchema,
} from '@modelcontextprotocol/sdk/types.js'
import { tools } from './tools.js'
import { handleToolCall } from './handlers.js'

const server = new Server(
  {
    name: 'postgres-mcp-server',
    version: '1.0.0',
  },
  {
    capabilities: {
      tools: {},
    },
  }
)

// List available tools
server.setRequestHandler(ListToolsRequestSchema, async () => ({
  tools: tools.map(tool => ({
    name: tool.name,
    description: tool.description,
    inputSchema: tool.inputSchema,
  })),
}))

// Handle tool calls
server.setRequestHandler(CallToolRequestSchema, async (request) => {
  const { name, arguments: args } = request.params

  try {
    const result = await handleToolCall(name, args)
    return {
      content: [
        {
          type: 'text',
          text: JSON.stringify(result, null, 2),
        },
      ],
    }
  } catch (error) {
    return {
      content: [
        {
          type: 'text',
          text: `Error: ${error instanceof Error ? error.message : String(error)}`,
        },
      ],
      isError: true,
    }
  }
})

// Start server
async function main() {
  const transport = new StdioServerTransport()
  await server.connect(transport)
  console.error('Postgres MCP server running')
}

main().catch(console.error)

---

Step 3: Creating Tool Handlers

Now let's create tool definitions and handlers.

Define Tools

// src/tools.ts
export interface Tool {
  name: string
  description: string
  inputSchema: {
    type: string
    properties: Record<string, any>
    required?: string[]
  }
}

export const tools: Tool[] = [
  {
    name: 'get_customer_info',
    description: 'Get customer information for support context. Returns customer details, subscription status, recent usage, and open tickets.',
    inputSchema: {
      type: 'object',
      properties: {
        email: {
          type: 'string',
          description: 'Customer email address',
        },
      },
      required: ['email'],
    },
  },
  {
    name: 'get_customer_analytics',
    description: 'Get aggregated customer analytics (no PII). Returns plan-level metrics and trends.',
    inputSchema: {
      type: 'object',
      properties: {
        plan_name: {
          type: 'string',
          description: 'Plan name to filter by (optional)',
        },
      },
    },
  },
]

Create Tool Handlers

// src/handlers.ts
import { pool } from './database.js'

// Input validation
function validateEmail(email: string): boolean {
  const emailRegex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/
  return emailRegex.test(email)
}

// Get customer info handler
async function getCustomerInfo(email: string) {
  // Validate input
  if (!validateEmail(email)) {
    throw new Error('Invalid email format')
  }

  // Execute parameterized query
  const query = `
    SELECT * 
    FROM customer_support_view 
    WHERE email = $1 
    LIMIT 1
  `

  const startTime = Date.now()
  const result = await pool.query(query, [email])
  const duration = Date.now() - startTime

  // Log query
  console.error({
    tool: 'get_customer_info',
    email,
    duration,
    rows: result.rows.length,
  })

  if (result.rows.length === 0) {
    return { message: 'Customer not found' }
  }

  return result.rows[0]
}

// Get customer analytics handler
async function getCustomerAnalytics(planName?: string) {
  let query = `
    SELECT 
      plan_name,
      subscription_status,
      COUNT(*) as customer_count,
      AVG(active_users_30d) as avg_active_users,
      SUM(total_revenue) as total_revenue
    FROM customer_analytics_view
  `
  const params: any[] = []

  if (planName) {
    query += ' WHERE plan_name = $1'
    params.push(planName)
  }

  query += ' GROUP BY plan_name, subscription_status'

  const startTime = Date.now()
  const result = await pool.query(query, params)
  const duration = Date.now() - startTime

  // Log query
  console.error({
    tool: 'get_customer_analytics',
    planName,
    duration,
    rows: result.rows.length,
  })

  return result.rows
}

// Main handler router
export async function handleToolCall(name: string, args: any): Promise<any> {
  switch (name) {
    case 'get_customer_info':
      if (!args?.email) {
        throw new Error('Email is required')
      }
      return await getCustomerInfo(args.email)

    case 'get_customer_analytics':
      return await getCustomerAnalytics(args?.plan_name)

    default:
      throw new Error(`Unknown tool: ${name}`)
  }
}

---

Step 4: Adding Security Layers

Now let's add security layers to protect your Postgres database.

Query Timeout

Add query timeouts to prevent runaway queries:

// src/database.ts
import { Pool, PoolConfig } from 'pg'

const poolConfig: PoolConfig = {
  // ... existing config
  statement_timeout: 30000, // 30 seconds
  query_timeout: 30000,
}

export const pool = new Pool(poolConfig)

Input Validation

Validate all inputs to prevent SQL injection:

// src/handlers.ts

function validateEmail(email: string): boolean {
  const emailRegex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/
  return emailRegex.test(email)
}

function validateUUID(uuid: string): boolean {
  const uuidRegex = /^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$/i
  return uuidRegex.test(uuid)
}

function sanitizeString(input: string): string {
  // Remove potentially dangerous characters
  return input.replace(/[;'"]/g, '')
}

Query Whitelisting

Only allow queries on specific views:

// src/handlers.ts

const ALLOWED_VIEWS = [
  'customer_support_view',
  'customer_analytics_view',
  'product_usage_view',
]

function validateView(viewName: string): boolean {
  return ALLOWED_VIEWS.includes(viewName)
}

async function executeQuery(viewName: string, filters: Record<string, any>) {
  if (!validateView(viewName)) {
    throw new Error(`View ${viewName} is not allowed`)
  }

  // Build parameterized query
  const conditions: string[] = []
  const params: any[] = []
  let paramIndex = 1

  for (const [key, value] of Object.entries(filters)) {
    conditions.push(`${key} = $${paramIndex}`)
    params.push(value)
    paramIndex++
  }

  const query = `SELECT * FROM ${viewName} WHERE ${conditions.join(' AND ')} LIMIT 100`

  return await pool.query(query, params)
}

Rate Limiting

Add rate limiting to prevent abuse:

// src/rate-limiter.ts
import { LRUCache } from 'lru-cache'

const rateLimitCache = new LRUCache<string, number[]>({
  max: 1000,
  ttl: 60000, // 1 minute
})

export function checkRateLimit(identifier: string, maxRequests: number = 60): boolean {
  const now = Date.now()
  const windowStart = now - 60000 // 1 minute window

  const requests = rateLimitCache.get(identifier) || []
  const recentRequests = requests.filter(time => time > windowStart)

  if (recentRequests.length >= maxRequests) {
    return false // Rate limit exceeded
  }

  recentRequests.push(now)
  rateLimitCache.set(identifier, recentRequests)
  return true
}

Audit Logging

Log all queries for audit trails:

// src/audit.ts
interface AuditLog {
  timestamp: string
  tool: string
  user?: string
  parameters: Record<string, any>
  duration: number
  rows: number
  error?: string
}

export async function logQuery(log: AuditLog) {
  // Log to file, database, or external service
  console.error(JSON.stringify(log))

  // Or insert into audit table
  // await pool.query(
  //   'INSERT INTO audit_logs (timestamp, tool, user, parameters, duration, rows, error) VALUES ($1, $2, $3, $4, $5, $6, $7)',
  //   [log.timestamp, log.tool, log.user, JSON.stringify(log.parameters), log.duration, log.rows, log.error]
  // )
}

---

Step 5: Deploying to Production

Now let's deploy the MCP server to production.

Build for Production

// package.json
{
  "scripts": {
    "build": "tsc",
    "start": "node dist/server.js",
    "dev": "tsx src/server.ts"
  }
}

# Build
npm run build

# Start
npm start

Deploy to Vercel

// vercel.json
{
  "version": 2,
  "builds": [
    {
      "src": "dist/server.js",
      "use": "@vercel/node"
    }
  ],
  "routes": [
    {
      "src": "/(.*)",
      "dest": "dist/server.js"
    }
  ]
}

Deploy to Railway

# Dockerfile
FROM node:18-alpine

WORKDIR /app

COPY package*.json ./
RUN npm ci --only=production

COPY dist ./dist

CMD ["node", "dist/server.js"]

Environment Variables

Set environment variables in your hosting platform:

POSTGRES_HOST=your-postgres-host
POSTGRES_PORT=5432
POSTGRES_DATABASE=your_database
POSTGRES_USER=agent_role
POSTGRES_PASSWORD=secure-password
POSTGRES_SSL=true
POSTGRES_MAX_CONNECTIONS=20

Health Checks

Add health check endpoint:

// src/server.ts

// Health check
server.setRequestHandler(HealthCheckRequestSchema, async () => {
  try {
    await pool.query('SELECT 1')
    return { status: 'healthy' }
  } catch (error) {
    return { status: 'unhealthy', error: error.message }
  }
})

---

Real-World Examples

Let me show you real-world implementations:

Example 1: Support Agent MCP Server

Requirements:

• Support agents answer customer questions
• Access customer data for support context
• Cannot access other customers' data
• Cannot access financial data

Implementation:

1. Postgres View:

CREATE VIEW customer_support_view AS
SELECT 
  id,
  name,
  email,
  plan_name,
  subscription_status,
  last_login_date,
  active_users_30d,
  open_tickets_count
FROM customers
WHERE is_active = true
  AND signup_date >= CURRENT_DATE - INTERVAL '2 years';

CREATE INDEX idx_customer_support_email ON customers(email) WHERE is_active = true;

GRANT SELECT ON customer_support_view TO agent_role;

2. MCP Tool:

{
  name: 'get_customer_info',
  description: 'Get customer information for support context',
  inputSchema: {
    type: 'object',
    properties: {
      email: { type: 'string', description: 'Customer email address' }
    },
    required: ['email']
  }
}

3. Tool Handler:

async function getCustomerInfo(email: string) {
  if (!validateEmail(email)) {
    throw new Error('Invalid email format')
  }

  const result = await pool.query(
    'SELECT * FROM customer_support_view WHERE email = $1 LIMIT 1',
    [email]
  )

  return result.rows[0] || { message: 'Customer not found' }
}

Result: Support agents get complete customer context without accessing other customers' data or financial information.

Example 2: Analytics Agent MCP Server

Requirements:

• Analytics agents generate insights
• Access aggregated customer data
• Cannot access PII
• Cannot access individual customer records

Implementation:

1. Postgres View (Materialized):

CREATE MATERIALIZED VIEW customer_analytics_view AS
SELECT 
  plan_name,
  subscription_status,
  COUNT(*) as customer_count,
  AVG(active_users_30d) as avg_active_users,
  SUM(total_revenue) as total_revenue,
  AVG(order_count) as avg_orders
FROM customers_aggregated
WHERE is_active = true
GROUP BY plan_name, subscription_status
HAVING COUNT(*) >= 10;

CREATE INDEX idx_customer_analytics_plan ON customer_analytics_view(plan_name);

GRANT SELECT ON customer_analytics_view TO agent_role;

-- Refresh periodically
REFRESH MATERIALIZED VIEW customer_analytics_view;

2. MCP Tool:

{
  name: 'get_customer_analytics',
  description: 'Get aggregated customer analytics (no PII)',
  inputSchema: {
    type: 'object',
    properties: {
      plan_name: { type: 'string', description: 'Plan name to filter by (optional)' }
    }
  }
}

3. Tool Handler:

async function getCustomerAnalytics(planName?: string) {
  let query = 'SELECT * FROM customer_analytics_view'
  const params: any[] = []

  if (planName) {
    query += ' WHERE plan_name = $1'
    params.push(planName)
  }

  const result = await pool.query(query, params)
  return result.rows
}

Result: Analytics agents get insights without accessing individual customer data or PII.

Example 3: Multi-Tenant Agent MCP Server

Requirements:

• Agents access tenant data
• Complete isolation between tenants
• Agents can only access their tenant's data

Implementation:

1. Postgres View (Tenant-scoped):

CREATE OR REPLACE FUNCTION get_tenant_customers(tenant_id_param UUID)
RETURNS TABLE (
  id UUID,
  name TEXT,
  email TEXT,
  plan_name TEXT,
  subscription_status TEXT
) AS $$
BEGIN
  RETURN QUERY
  SELECT 
    c.id,
    c.name,
    c.email,
    c.plan_name,
    c.subscription_status
  FROM customers c
  WHERE c.tenant_id = tenant_id_param
    AND c.is_active = true;
END;
$$ LANGUAGE plpgsql SECURITY DEFINER;

GRANT EXECUTE ON FUNCTION get_tenant_customers(UUID) TO agent_role;

CREATE INDEX idx_customers_tenant ON customers(tenant_id) WHERE is_active = true;

2. MCP Tool:

{
  name: 'get_tenant_customers',
  description: 'Get customers for current tenant',
  inputSchema: {
    type: 'object',
    properties: {
      tenant_id: { type: 'string', description: 'Tenant ID (from conversation context)' }
    },
    required: ['tenant_id']
  }
}

3. Tool Handler:

async function getTenantCustomers(tenantId: string) {
  if (!validateUUID(tenantId)) {
    throw new Error('Invalid tenant ID format')
  }

  const result = await pool.query(
    'SELECT * FROM get_tenant_customers($1)',
    [tenantId]
  )

  return result.rows
}

Result: Complete tenant isolation. Agents can only access their tenant's data.

---

Postgres-Specific Best Practices

Here are best practices specific to Postgres:

1. Use Connection Pooling

Always use connection pooling for agent queries:

const pool = new Pool({
  max: 20, // Max connections in pool
  idleTimeoutMillis: 30000,
  connectionTimeoutMillis: 2000,
})

Why: Prevents connection exhaustion. One pool handles all agent queries.

2. Use Read Replicas

Route agent queries to read replicas, not production:

// Use replica connection string
const pool = new Pool({
  host: 'replica.postgres.example.com',
  // ... other config
})

Why: Agent queries don't impact production. Production stays fast for customer-facing services.

3. Use Parameterized Queries

Always use parameterized queries to prevent SQL injection:

// ✅ Good: Parameterized
await pool.query('SELECT * FROM customers WHERE email = $1', [email])

// ❌ Bad: String concatenation
await pool.query(`SELECT * FROM customers WHERE email = '${email}'`)

Why: Parameterized queries prevent SQL injection attacks.

4. Set Query Timeouts

Set query timeouts to prevent runaway queries:

const pool = new Pool({
  statement_timeout: 30000, // 30 seconds
  query_timeout: 30000,
})

Why: Prevents one slow query from locking your entire database.

5. Use Materialized Views for Analytics

For analytical queries, use materialized views:

CREATE MATERIALIZED VIEW customer_metrics AS
SELECT 
  customer_id,
  COUNT(*) as order_count,
  SUM(amount) as total_revenue
FROM orders
GROUP BY customer_id;

-- Refresh periodically
REFRESH MATERIALIZED VIEW customer_metrics;

Why: Materialized views pre-aggregate data, making analytical queries fast and cheap.

6. Monitor Query Performance

Track query performance to identify slow queries:

-- Enable query logging
ALTER SYSTEM SET log_min_duration_statement = 1000; -- Log queries > 1 second
SELECT pg_reload_conf();

-- Check slow queries
SELECT 
  query,
  calls,
  total_exec_time,
  mean_exec_time,
  max_exec_time
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 10;

Why: Identifying slow queries helps you optimize performance and reduce costs.

7. Use Roles for Access Control

Create dedicated roles for agents with limited permissions:

-- Create agent role
CREATE ROLE agent_role WITH LOGIN PASSWORD 'secure-password';

-- Grant only necessary permissions
GRANT SELECT ON customer_support_view TO agent_role;
-- Never grant: GRANT ALL ON customers TO agent_role;

Why: Roles limit what agents can access. Principle of least privilege.

---

Common Mistakes to Avoid

Here are mistakes I've seen teams make:

Mistake 1: Not Using Connection Pooling

What happens: Each agent instance opens a Postgres connection, exhausting the connection limit.

Why it's a problem: Connection exhaustion prevents other applications from connecting.

The fix: Use connection pooling. MCP server manages a single pool for all agents.

Mistake 2: Exposing Production Database

What happens: Teams connect agents directly to production Postgres.

Why it's a problem: Agent queries impact production performance. One slow query can crash customer-facing services.

The fix: Use read replicas for agent queries. Never connect agents directly to production.

Mistake 3: Not Using Parameterized Queries

What happens: Tools build queries using string concatenation, creating SQL injection risks.

Why it's a problem: SQL injection can lead to data breaches and unauthorized access.

The fix: Always use parameterized queries. Never concatenate user input into SQL.

Mistake 4: No Query Timeouts

What happens: Queries run indefinitely, locking tables and blocking other queries.

Why it's a problem: One slow query can lock your entire database.

The fix: Set query timeouts. 30 seconds is a good default.

Mistake 5: Giving Agents Too Many Permissions

What happens: Teams give agents superuser access or access to all tables.

Why it's a problem: Agents can access data they shouldn't, violating compliance requirements.

The fix: Create dedicated roles with limited permissions. Grant access only to views, not tables.

Mistake 6: Not Monitoring Performance

What happens: Teams deploy MCP server and don't monitor query performance.

Why it's a problem: Performance issues go unnoticed. Costs spike. Users get frustrated.

The fix: Monitor query performance, costs, and access patterns from day one. Set up alerts for anomalies.

Mistake 7: Static Tool Definitions

What happens: Tools are created once and never updated.

Why it's a problem: As agents evolve, tools become outdated. Over-permissive or under-permissive access.

The fix: Review and update tools regularly. Remove unused tools, optimize existing tools, add new tools as needed.

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Where Pylar Fits In

Pylar makes building a Postgres MCP server practical. Here's how:

Postgres Integration: Pylar connects directly to Postgres. You provide your Postgres connection string, and Pylar handles connection pooling, query optimization, and error handling. No infrastructure to manage.

Sandboxed Views: Pylar's SQL IDE lets you create sandboxed views that define exactly what agents can access. Views can join data across multiple tables, filter rows and columns, and enforce compliance requirements. Views are version-controlled and auditable.

MCP Tool Builder: Pylar automatically generates MCP tools from your views. Describe what you want in natural language, and Pylar creates the tool definition, parameter validation, and query logic. No backend engineering required.

Connection Management: Pylar manages Postgres connections efficiently. Connection pooling, read replica routing, and query optimization are built in. You don't have to worry about connection exhaustion or performance issues.

Query Optimization: Pylar views are optimized for agent queries. Pylar suggests indexes, pre-aggregations, and query optimizations that keep Postgres costs low and performance fast.

Access Control: Pylar enforces access control on every query. Before executing a query, Pylar validates role, context, and permissions. Queries that violate access boundaries are rejected.

Monitoring: Pylar Evals tracks query performance, costs, and access patterns. You can see exactly how agents are using your Postgres layer, which queries are most expensive, and where optimization opportunities exist.

Framework-Agnostic: Pylar tools work with any MCP-compatible framework. Whether you're using Claude Desktop, LangGraph, OpenAI, n8n, or any other framework, Pylar provides the same safe layer.

Pylar is the Postgres MCP server you don't have to build. Instead of building custom connection pooling, view management, and tool generation, you build views and tools in Pylar. The MCP server is built in.

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Building a Postgres MCP server isn't optional—it's essential. Without it, agents can exhaust connection pools, write dangerous queries, and create security risks. Start with connection pooling, add sandboxed views, create tools, and monitor continuously.

The MCP server pattern gives you the foundation. Manage connections efficiently, govern access through views, and provide agent-friendly interfaces through tools. With proper implementation, agents become secure, performant business tools rather than Postgres risks.