Understand MCP — Anthropic's open standard for connecting AI models to external tools and data sources. Learn how MCP differs from function calling, why it's becoming the USB-C of AI integration, and how Indian companies can leverage it.
MCP (Model Context Protocol) is Anthropic's open standard that lets AI models like Claude securely connect to external tools, databases, and APIs through a standardized interface. Unlike function calling which is model-specific, MCP works across any AI model and provides a universal connector — like USB-C for AI. Boolean & Beyond is among the first companies in India implementing MCP for enterprise clients.
Model Context Protocol (MCP) is an open standard created by Anthropic that defines how AI models connect to external tools, data sources, and services. It acts as a universal API layer for AI, so instead of building custom integrations for every system (Salesforce, Jira, SAP, internal databases), you expose them once via MCP and any MCP-compatible AI client can use them.
MCP standardizes how AI assistants discover, call, and securely interact with business systems, enabling real-time, bidirectional access to tools and data.
Traditionally, connecting an AI assistant to enterprise tools required custom, one-off integrations for each system, leading to high development and maintenance costs.
MCP changes this by providing:
MCP has three main components:
The AI application that needs external capabilities (e.g., Claude Desktop, a custom chatbot, or an enterprise copilot). It understands the MCP protocol and can discover and call tools, resources, and prompts exposed by servers.
A lightweight service that wraps a specific system (Salesforce, SAP, Jira, databases, internal APIs) and exposes them via MCP. It defines tools, resources, and prompts with clear schemas.
The communication channel between client and server:
When a user asks something like “What’s the status of ticket JIRA-1234?”, the AI client:
An MCP server exposes three capability types:
Tools are operations the AI can perform.
search_salesforce, create_jira_ticket, query_database, send_slack_message.Resources are data sources the AI can fetch on demand.
Prompts are pre-configured interaction templates that can orchestrate multiple steps.
MCP is designed with enterprise-grade security in mind, with controls at multiple layers:
MCP supports several transport mechanisms between clients and servers:
A Salesforce MCP server connects AI to your CRM for real-time sales intelligence.
Example tools:
search_accounts: Find accounts by name, industry, revenue range.get_opportunity_details: Retrieve deal stage, amount, close date, owner.update_opportunity_stage: Move deals through pipeline stages.search_contacts: Find contacts by company, role, or interaction history.create_task: Create follow-up tasks for sales reps.Typical use cases:
An SAP MCP server connects AI to your ERP for operational and financial intelligence.
Example tools:
check_inventory: Get real-time stock levels across warehouses.get_purchase_order_status: Track PO delivery and payment status.query_financial_reports: Pull P&L, balance sheet, cash flow data.check_production_orders: View manufacturing order status and progress.Typical use cases:
A Jira MCP server connects AI to your project management system for engineering and delivery insights.
Example tools:
search_issues: Find tickets by project, assignee, status, labels.get_sprint_status: Get sprint progress, burndown, and blockers.create_issue: Create new tickets with appropriate fields and assignments.update_issue_status: Move tickets through workflow states.get_release_notes: Aggregate completed work for a release.Typical use cases:
Start with systems that consume the most time and context-switching:
For each target system:
Typical stack:
MCP servers can be consumed by:
claude_desktop_config.json to register servers.Approximate ranges for Indian engineering teams in 2025:
Rs 2–5 lakh, ~2–3 weeks.
Rs 8–15 lakh, ~4–6 weeks.
Rs 25–50 lakh, ~8–12 weeks.
For a sales team of ~20 using Salesforce + Slack via MCP:
Boolean & Beyond specializes in building production-grade MCP servers for Indian enterprises.
By standardizing integrations through MCP, organizations can move from experimental AI pilots to robust, scalable AI copilots that work across their entire tool stack.
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