What is Model Context Protocol (MCP) and Why It Matters

MCP has three main components:

1. MCP Client

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.

1. MCP Server

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.

1. Transport Layer

The communication channel between client and server:

• Stdio for local servers (simple, low overhead)
• HTTP + SSE for remote, multi-user, authenticated setups
• Streamable HTTP for request–response plus streaming updates

When a user asks something like “What’s the status of ticket JIRA-1234?”, the AI client:

• Infers it should use a Jira-related tool.
• Calls the appropriate MCP server tool.
• The server talks to Jira’s API.
• The result is returned to the AI in under a couple of seconds.

An MCP server exposes three capability types:

Tools (Actions)

Tools are operations the AI can perform.

• Examples: search_salesforce, create_jira_ticket, query_database, send_slack_message.
• Each tool has a schema: input parameters and output format.
• The AI chooses when and how to call tools based on user intent.

Resources (Readable Data)

Resources are data sources the AI can fetch on demand.

• Examples: knowledge bases, product catalogs, policy documents.
• Exposed as URIs that the AI can request.
• Support pagination for large datasets.

Prompts (Workflow Templates)

Prompts are pre-configured interaction templates that can orchestrate multiple steps.

• Examples: “Analyze this sales pipeline”, “Generate a sprint report”.
• Can combine multiple tool calls into structured workflows.
• Ideal for complex, multi-step business processes.

MCP is designed with enterprise-grade security in mind, with controls at multiple layers:

• Authentication: Supports OAuth 2.0, API keys, or custom mechanisms per server.
• Authorization: Fine-grained permissions per tool and resource (e.g., read-only Salesforce access, no delete operations).
• Input validation: All tool inputs are validated against schemas before execution.
• Audit logging: Every tool call can be logged with user identity, timestamp, parameters, and results.
• Rate limiting: Protects backend systems from overload by controlling call frequency.

MCP supports several transport mechanisms between clients and servers:

• Stdio (Standard I/O)
• Ideal for local servers running on the same machine.
• No network overhead, simplest to set up.
• HTTP + Server-Sent Events (SSE)
• Best for remote servers and multi-user environments.
• Works well with authentication, load balancing, and enterprise networking.
• Streamable HTTP
• Combines traditional request–response with streaming.
• Useful for long-running operations and real-time progress updates.

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:

• “Show me all open opportunities over Rs 50 lakh closing this quarter.”
• “What’s the history of our interactions with Infosys?”
• “Create a follow-up task for the TCS deal review.”

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:

• “What’s our current stock of raw material X across all warehouses?”
• “Show me all overdue purchase orders from the last 30 days.”
• “What’s our gross margin trend for the last 4 quarters?”

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:

• “What are the blockers in the current sprint?”
• “Create a bug ticket for the login page crash on iOS.”
• “Generate release notes for version 2.5.”

Step 1: Identify High-Value Integrations

Start with systems that consume the most time and context-switching:

• CRM (Salesforce, HubSpot): Reduce manual data entry and lookups for sales teams.
• Project management (Jira, Asana): Help engineering teams avoid tool-hopping.
• Communication (Slack, Teams): Summarize threads, extract action items, route queries.
• Databases: Provide controlled SQL access for analytics (often read-only).
• Internal documentation (Confluence, Notion): Turn documentation into an AI-powered knowledge base.

Step 2: Design the MCP Server

For each target system:

• Define tools: What actions should AI be able to perform?
• Define resources: What data should AI be able to read on demand?
• Set permissions: Who can use which tools? Read-only vs. read–write?
• Plan error handling: How should failures be surfaced if the external API is down or slow?
• Design rate limiting: How many calls per minute per user or per tool?

Step 3: Build and Deploy

Typical stack:

• Languages: TypeScript (official SDK), Python (official SDK), or any language that can speak JSON-RPC.
• Hosting: Docker containers on AWS, GCP, Azure, or on-prem behind VPN.
• Monitoring: Prometheus or similar for latency, error rates, and usage metrics.
• Testing: Integration tests against sandbox environments (e.g., Salesforce Sandbox, Jira test projects).

Step 4: Connect to AI Clients

MCP servers can be consumed by:

• Claude Desktop: Configure via claude_desktop_config.json to register servers.
• Custom AI applications: Use the MCP client SDKs (TypeScript or Python) to connect.
• Enterprise copilots: Multi-server setups where a single copilot orchestrates across Salesforce, SAP, Jira, and internal tools.

Development Costs

Approximate ranges for Indian engineering teams in 2025:

• Simple MCP server (single tool, read-only):

Rs 2–5 lakh, ~2–3 weeks.

• Standard MCP server (5–10 tools, read–write):

Rs 8–15 lakh, ~4–6 weeks.

• Enterprise MCP suite (multiple systems, custom auth, audit):

Rs 25–50 lakh, ~8–12 weeks.

Ongoing Costs

• Hosting: Rs 5,000–20,000 per month per server (cloud, typical usage).
• Maintenance: ~10–15% of development cost annually (API changes, security updates, enhancements).
• AI API costs: Based on usage volume and model pricing (e.g., Claude API).

ROI Example

For a sales team of ~20 using Salesforce + Slack via MCP:

• Time saved: 1–2 hours per person per day on lookups and data entry.
• Annual value: Roughly Rs 30–50 lakh in recovered productive time.
• Payback period: Often within 2–4 months after go-live.

Boolean & Beyond specializes in building production-grade MCP servers for Indian enterprises.

• Experience integrating AI with Salesforce, SAP, Jira, custom databases, and internal tools.
• End-to-end delivery: MCP server architecture, implementation, deployment, and AI client integration.
• Focus on security, observability, and maintainability, so your teams get AI-powered access to tools without dealing with protocol complexity.

By standardizing integrations through MCP, organizations can move from experimental AI pilots to robust, scalable AI copilots that work across their entire tool stack.

Explore more from our AI solutions library:

• Building a Private ChatGPT for Your Company — Deploy an enterprise AI copilot trained on your internal documents using private LLMs and RAG architecture.
• RAG Implementation & Vector Databases — Learn how retrieval-augmented generation works and how to choose the right vector database for production.