Data to Decisions

Introduction

Enterprises today struggle with fragmented systems—isolated databases, legacy APIs, siloed documents. Meanwhile, AI agents are getting smarter—but without real-time data access, they can only ever be half-informed. Enter the powerful trio:

▸ MCP (Model Context Protocol) for seamless, secure connectivity

▸ PostgreSQL as the enterprise backbone

▸ RAG (Retrieval-Augmented Generation) for deep, relevant insight

We can also add External APIs to bring third-party data into context to the above combination. Combining these enables AI agents to navigate complex queries and business logic with precision—and in conversational language.

1. MCP: The Universal Bridge

The Model Context Protocol is an open, JSON-RPC‑based standard introduced by Anthropic in late 2024, represents an advancement in AI integration, functioning as the “USB-C port for AI agents” by providing a universal standard for connecting AI models with external tools and data sources.

This means developers can build more capable, context-aware applications without reinventing the wheel for each combination of AI model and data sources.

Why it matters

Standardization – Replace N×M integration headaches with a unified protocol

Security baseline – Tool permissions can be scoped per function; future frameworks (MCP Guardian, Gateway) add auth, rate limiting, and auditing

Tool orchestration – AI seamlessly chains multiple tools—schema lookups, queries, API calls

MCP’s Architecture

MCP follows a classic client-server model where:

Host Application – your AI client (e.g., Claude Desktop, Copilot, or ChatGPT UI).

MCP Client – embedded within the host, handling discovery and RPC calls.

MCP Server – program that interfaces with data sources, services or APIs and exposes the following standardized capabilities or actions to the MCP clients

▸ Tools –  Functions that can be invoked by AI models to fetch information or perform actions like sending a message or updating database records.

▸ Resources – Read-only endpoints, such as fetching documents, configuration, or datasets.

▸ Prompts – Pre-defined templates that an application user can request for effective AI interactions

2. MCP + PostgreSQL: A Conversational Database Interface

Imagine asking your AI assistant

▸ Show me this month’s high-value orders and the top three customers.

▸ The assistant understands your intent, crafts SQL queries behind the scenes, and executes them securely—thanks to the PostgreSQL MCP server.

What is PostgreSQL MCP Server

An MCP-compliant PostgreSQL server exposes structured database operations—like reading rows, inserting data, or describing schemas—as callable tools. This means your AI agent doesn’t need raw SQL privileges—it just uses allowed, structured methods defined by the MCP server.

Capabilities include:

▸ Schema discovery: list_tables, describe_table

▸ CRUD: insert_row, read_rows, update_row, delete_row

▸ Controlled queries with binding and output limits

How it works

1. AI Agent (client) asks, “Show me this month’s high-value orders and the top three customers.”

2. MCP‑Postgres server returns JSON, agent integrates that into conversational context.

Available PostgreSQL MCP Servers

3. Enhancing Intelligence with RAG

Retrieval-Augmented Generation (RAG) is an AI framework that combines the strengths of traditional information retrieval systems (such as search and database) with the capabilities of AI models that are outstanding at natural language generation. 

MCP alone gives structured access. To truly supercharge AI responses, layer in Retrieval‑Augmented Generation (RAG):

1. Vectorize unstructured data—docs, logs, knowledge bases in Vector DB

2. Agent uses RAG system to identify relevant context based on the user query

3. Combine with Postgres queries to enrich answers

Example workflow:

User: “Which customer had returns last month, and what policies applied?”

1. Agent issues MCP call read_rows on returns table

2. Finds clients A, B, C

3. RAG fetches relevant policy text chunks for these clients

4. Agent composes answer: orders + policy summaries + recommendations

4. Extending with External APIs

Say you need recent currency rates, latest weather situation, ERP integration, or ticketing data. Just expose them via MCP too.

▸ Build a lightweight MCP server wrapping an external API

▸ Implement tools like get_exchange_rate, get_ticket_status, send_notification

Example workflow:

▸ Agent: calls get_exchange_rate(from=”USD”, to=”INR”)

▸ Agent: “₹84.50 is current rate.”

▸ Then queries Postgres for expenses, converts order value to INR.

5. Powerful combination

The diagram illustrates a modular AI architecture where an AI Agent communicates with a composite layer of MCP servers—PostgreSQL, RAG-based vector store, and external APIs—enabling it to fetch, reason, and respond using real-time structured, unstructured, and third-party data sources in one seamless flow.

Workflow example:

1. User Question: Identify high-risk customers who placed orders over ₹50,000 last month and summarize their support ticket issues.

2. AI agent → PostgreSQL MCP Server: read_rows(table=’orders’, filters=[amount     > 50000, date_in_last_30_days])

3. AI agent → API MCP Server: get_ticket_summary(customer_ids=[…])

4. AI agent → Vector Store: Embeds ticket descriptions, retrieves related past complaints or policies using semantic search

5. AI agent synthesizes: Combines order data, complaint history, and RAG context to produce:

a. Customer profile

b. Risk score (based on order volume + complaints)

c. Recommended actions

6. AI returns output: Structured summary in json format

   {

  “customer”: “Amit Kapoor”,

  “total_order_value”: “₹82,300”,

  “risk_factors”: [“3 unresolved tickets”, “repeat returns”],

  “action”: “Escalate to CX Manager”

   }

6. Road Ahead

▸ Cache Smartly: Not every cache need to be generated every time like ProductCatalog. 

▸ Monitoring: Implement dashboards for analyzing the query and LLMs performance and find ways to improve.

▸ Audio Layer: Can add Audio layer on top of text input

7. Frameworks

▸ Langgraph – defining workflow

▸ N8n – No Code platform alternative to Langgraph with promising UI features

▸ Langchain – LLM Tool orchestration framework

▸ PostgreSQL MCP server – generating SQL queries