Skip to content

Integration guides

AI integration guides

Step-by-step guides for connecting AI agents to databases, APIs, and enterprise tools via MCP.

Why integration matters

Model Context Protocol (MCP) provides a standardized way to connect agents to external systems — databases, APIs, file services, and enterprise tools — enabling workflows that combine AI reasoning with real-world data and actions.

For Indian deployments, determine whether personal data is processed and review the current DPDP Act, rules, government notifications, contracts, and security controls with qualified counsel. This page does not infer a blanket India-only data-residency rule or treat consent as the sole basis for every cross-border transfer.

These integration outlines are review checklists, not tested copy-paste procedures. They remain noindex until each client, server, configuration path, and security step is checked against current first-party documentation.

Before you start any of these guides

Every guide below assumes an MCP-compatible client is already installed and running — the steps cover connecting that client to a specific server, not installing the client itself. If a step references a config file path (like ~/.cline_mcp_settings.json), confirm the current path against that client's own documentation first: client config locations change across versions more often than the underlying MCP connection steps do.

Read-only first, write access second

Where a guide below touches a database, a payment system, or a code repository, start with the most restrictive access mode available — read-only database credentials, a scoped-down GitHub token, sandbox-mode payment keys — before granting write access. An agent that can only read cannot accidentally execute a destructive query, merge an unreviewed pull request, or trigger a real payment; expand scope only after you have watched the agent operate correctly under the restrictive mode.

Common failure points

Most first-time MCP integration failures fall into a small number of categories: a transport mismatch (client configured for stdio while the server expects Streamable HTTP, or vice versa), a credential with the wrong scope (a GitHub token missing the repo scope, a database user without SELECT on the target schema), or a firewall/network boundary blocking a remote server the client expects to reach directly. When a connection fails, check these three before assuming the server itself is broken — most MCP servers log a clear error for exactly this class of misconfiguration.

Setting Up MCP with Cline

Beginner

Step-by-step guide to configuring Model Context Protocol with Cline, the open-source VS Code agent.

  1. Install Cline from the VS Code marketplace
  2. Create or update your MCP configuration file (~/.cline_mcp_settings.json)
  3. Add your MCP server configuration with the appropriate transport (stdio or HTTP)
  4. Restart Cline and verify the connection in the MCP explorer panel
  5. Test tool calls to ensure the integration is working correctly

Configuring Cursor with Streamable HTTP

Intermediate

Guide to connecting Cursor AI IDE to remote MCP servers using Streamable HTTP transport.

  1. Ensure you have a Cursor version with MCP support
  2. Open Cursor Settings and navigate to the MCP section
  3. Add a new MCP server with Streamable HTTP transport
  4. Configure the server URL and authentication (API key or OAuth)
  5. Enable the server and verify tool availability in Cursor chat

Database Integration with PostgreSQL

Intermediate

Connecting AI agents to PostgreSQL databases using MCP for secure, read-only database access.

  1. Install the PostgreSQL MCP server (e.g. modelcontextprotocol/server-postgres)
  2. Configure the connection string with appropriate credentials
  3. Set up read-only access for security
  4. Test the connection with a simple query
  5. Configure query limits and timeout settings for production use

GitHub Integration for Code Repositories

Beginner

Integrating AI agents with GitHub for repository management, pull requests, and issue tracking.

  1. Install the GitHub MCP server from the official registry
  2. Generate a GitHub personal access token with appropriate scopes
  3. Configure the MCP server with your token and repository access
  4. Test repository operations (list issues, create PRs, review code)
  5. Set up branch protection and access controls for production use

Slack Bot Integration

Advanced

Building AI-powered Slack bots using MCP for message handling and workflow automation.

  1. Create a Slack app and configure bot token scopes
  2. Install the Slack MCP server and configure authentication
  3. Set up event subscriptions for message handling
  4. Implement workflow automation using MCP tools
  5. Deploy the bot to your Slack workspace and monitor usage

Indian Payment Integration (UPI)

Advanced

Integrating AI agents with Indian payment systems including UPI, payment gateways, and GST invoicing.

  1. Choose a payment gateway that supports UPI (Razorpay, Cashfree, or PayU)
  2. Configure the payment gateway MCP server with API keys
  3. Set up webhook endpoints for payment status updates
  4. Implement GST-compliant invoicing using the GST API
  5. Test the integration in sandbox mode before production deployment