Skip to content
Karyam

MCP Overview

Modern AI systems need more than knowledge and reasoning.

They need access to tools.

They need the ability to:

  • Create tickets
  • Manage repositories
  • Query databases
  • Access internal systems
  • Trigger workflows
  • Interact with SaaS platforms

Historically, every integration required custom code.

The Model Context Protocol (MCP) changes that.

What is MCP?

Section titled “What is MCP?”

MCP (Model Context Protocol) is an open standard for connecting AI systems to external tools and services.

Think of MCP as:

USB-C for AI applications.

Just as USB-C allows devices to connect using a standard interface, MCP allows AI systems to connect to tools using a standard protocol.

Why MCP Matters

Section titled “Why MCP Matters”

Without MCP:

AI Agent
    ↓
Custom Integration
    ↓
GitHub


AI Agent
    ↓
Custom Integration
    ↓
Jira


AI Agent
    ↓
Custom Integration
    ↓
Slack

Every new integration requires development effort.

With MCP:

AI Agent
    ↓
MCP
    ↓
GitHub Server
Jira Server
Slack Server
Database Server
Internal Tools

AI systems can discover and use tools dynamically.

MCP in Karyam

Section titled “MCP in Karyam”

Karyam is built with MCP as a first-class platform capability.

MCP Servers can be attached to:

  • Agents
  • AI Flows
  • Multi-agent systems

This allows AI systems to interact with business applications and external tools without requiring custom integrations.

High-Level Architecture

Section titled “High-Level Architecture”
User Request
        ↓
AI Agent
        ↓
MCP Client
        ↓
MCP Server
        ↓
Business System
        ↓
Response

Example:

Employee:
Create a GitHub issue for the VPN renewal problem.


Agent
    ↓
GitHub MCP Server
    ↓
Create Issue Tool
    ↓
Issue Created

What Can MCP Connect To?

Section titled “What Can MCP Connect To?”

The MCP ecosystem is growing rapidly.

Examples include:

Development Platforms

Section titled “Development Platforms”
  • GitHub
  • GitLab
  • Jira
  • Linear

Communication Platforms

Section titled “Communication Platforms”
  • Slack
  • Google Chat
  • Microsoft Teams

Productivity Tools

Section titled “Productivity Tools”
  • Notion
  • Confluence
  • Google Drive

Infrastructure & DevOps

Section titled “Infrastructure & DevOps”
  • Kubernetes
  • AWS
  • Docker
  • Terraform

Databases

Section titled “Databases”
  • PostgreSQL
  • MongoDB
  • Redis
  • Elasticsearch

Internal Systems

Section titled “Internal Systems”
  • ERP
  • HRMS
  • CRM
  • Internal APIs

MCP Servers vs Skills

Section titled “MCP Servers vs Skills”

This distinction is important.

Capability Skills MCP Servers
Managed by Karyam Yes No
Native platform capability Yes No
External ecosystem support Limited Yes
Uses an open protocol No Yes
Dynamic tool discovery No Yes

Think of it like this:

Skills

Section titled “Skills”

Capabilities built directly into Karyam.

Examples:

  • Email
  • Web Search
  • Database Access
  • Human Approvals

MCP Servers

Section titled “MCP Servers”

Capabilities exposed by external systems through the Model Context Protocol.

Examples:

  • GitHub MCP Server
  • Jira MCP Server
  • Slack MCP Server

Dynamic Tool Discovery

Section titled “Dynamic Tool Discovery”

One of MCP’s biggest advantages is discovery.

When Karyam connects to an MCP Server, it automatically discovers available tools.

Example:

GitHub MCP Server Connected
        ↓
Karyam Discovers:
- Create Issue
- Create Pull Request
- List Repositories
- Get Workflow Runs
- Search Code

No manual configuration is required.

MCP + AI Flows

Section titled “MCP + AI Flows”

MCP tools can be combined with AI Flows.

Example:

Production Incident
        ↓
Analyze Logs
        ↓
Create Jira Ticket
        ↓
Notify Team
        ↓
Request Approval
        ↓
Execute Fix

This transforms individual tools into complete AI systems.

MCP + Human Approvals

Section titled “MCP + Human Approvals”

Some actions require oversight.

Example:

Delete Production Database
        ↓
Approval Required
        ↓
Manager Approval
        ↓
Execute MCP Tool

This ensures powerful tools remain safe to use.

MCP + Observability

Section titled “MCP + Observability”

Every MCP execution is observable.

Karyam records:

  • Tool invocations
  • Inputs
  • Outputs
  • Errors
  • Execution duration

This provides complete visibility into AI actions.

Example Enterprise Architecture

Section titled “Example Enterprise Architecture”
Knowledge
    +
Agents
    +
Skills
    +
MCP Servers
    +
Flows
    +
Approvals
    +
Observability
        ↓
Production AI Systems

This is the Karyam approach.

Why Karyam Uses MCP

Section titled “Why Karyam Uses MCP”

Karyam believes AI ecosystems should be:

  • Open
  • Extensible
  • Interoperable
  • Vendor-neutral

MCP enables this future.

Instead of building hundreds of custom integrations, organizations can leverage a growing ecosystem of MCP-compatible tools and services.

Next Steps

Section titled “Next Steps”

Continue with:

  • What is MCP?
  • Connecting Servers
  • Authentication
  • Server Discovery
  • Examples