With OpenAI’s launch of the ChatGPT Agent — AI that can think, act, and proactively choose from a toolbox of skills to complete complex, multi-step tasks — we’ve hit a pivotal moment in AI development. This marks the shift from simple chat-based interfaces to task-oriented AI agents that can work autonomously and even collaborate with each other. As Gartner predicts, by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously. [1] But that raises an important question: how can enterprise AI agent integrations become more sophisticated, secure, and standardized?
That’s where the Model Context Protocol (MCP) for enterprises comes in. MCP gives enterprises a unified framework to connect AI agents with external systems, databases, cloud services, file systems, and enterprise applications. Paired with OneReach.ai’s Generative Studio X (GSX), an Agent platform, you can easily build an MCP Server in a low-code environment — empowering your AI agents to go beyond static language models, tap into live data, and perform real-world tasks.
Here, you’ll explore what MCP is and how it works, and the benefits it brings to enterprise agentic AI solutions. You’ll also learn about GSX’s MCP capabilities and get a step-by-step guide for integrating MCP in AI agents designed on the GSX platform — helping you transform scattered, custom solutions into a unified, scalable ecosystem.
What MCP Is and Why It Matters
The Model Context Protocol (MCP) is a universal open standard — think of it as the “USB-C port for AI applications.” Introduced in November 2024 by Anthropic and quickly adopted by major players such as OpenAI, Microsoft, and Google DeepMind, MCP enables standardized, secure integration between large language models (LLMs) and external systems, tools, and data – eliminating fragmented, bespoke connectors across a tech stack.
Unlike proprietary plugins that lock organizations into specific platforms, MCP provides a vendor-agnostic framework that ensures portability and interoperability across AI agents and platforms. This universal approach transforms how enterprises deploy AI by shifting from fragmented AI integrations to standardized, scalable agentic systems.
Why MCP Matters for Enterprise AI
MCP addresses three core enterprise challenges: integration complexity, security governance, and vendor lock-in. By establishing a common protocol for AI agent interactions, organizations can build and deploy AI agents across multiple platforms while maintaining consistent security controls and audit capabilities.
With an MCP Server, your LLM-powered applications gain:
- Access to real-time, proprietary or private data
- Seamless execution of business workflows (e.g., Git, Slack, CRM (Customer Relationship Management))
- Robust enforcement of access policies and audit trails
- Production-grade secure agent integrations.
Core MCP Architecture and Components
MCP operates on a client-server architecture inspired by the Language Server Protocol (LSP), utilizing JSON-RPC (JavaScript Object Notation – Remote Procedure Call) 2.0 for message formatting and supporting multiple transport mechanisms. The protocol’s design reflects a deliberate emphasis on AI-native capabilities, distinguishing it from traditional REST APIs (Representational State Transfer Application Programming Interfaces) that were primarily designed for human developers rather than autonomous agents.
The protocol is centered around four foundational components:
- Tools: Perform structured, safe actions like calling APIs, querying databases, or triggering workflows. Defined with JSON Schema for reliable invocation, tools are explicitly called by the LLM to ensure traceability and control.
- Resources: Structured read-only data such as metrics dashboards, catalogs, and document repositories that help the LLM “knows” what it needs to make intelligent decisions in real-time.
- Prompts: Server-hosted templates or workflows that are predefined and versioned to guide LLM behavior. Think of them as instruction blueprints with placeholders for dynamic inputs. Effective prompt management in MCP ensures these templates are organized, reusable, and easily updated, helping AI agents perform consistently and reliably.
- Authorization: Robust access control through token-based, OAuth (Open Authorization) 2.0, or policy-driven authentication. Provides audit logs, least-privilege enforcement, and defenses against tool or prompt injection.
How MCP Works
The MCP workflow follows a simple three-step process:
- Discover: Your AI (e.g., OneReach AI Agent, ChatGPT, or Claude) asks the MCP Server, “What can you do?” The server replies with a helpful menu of tools (actions it can perform), resources (data you can access), and prompts (pre-written instructions).
- Request: When your AI needs something — like a report or a team directory — it sends a standard request. This might be a call to a tool (“Fetch the latest report”) or a resource (“Give me the list of products”).
- Response: The server carries out the action and sends back structured information. The AI then uses that response to continue the conversation or take the next step — smoothly and securely.
This standardized approach ensures that AI agents can seamlessly interact with enterprise systems while maintaining security and governance requirements across different platforms and vendors.
Learn more about deploying secure, agentic AI systems with MCP
Download WhitepaperUsing OneReach.ai GSX Agent Platform and MCP for AI Agent Integrations
Key Advantages of GSX Agent Platform for MCP Integration
- Visual Development: Build an MCP server through intuitive drag-and-drop flows without coding expertise.
- Rapid Prototyping: Quickly iterate on tool definitions and business logic using visual components.
- Enterprise Integration: Seamlessly connect to existing workflows, databases, and third-party systems.
- Production Ready: Automatic handling of MCP protocol requirements, error handling, and response formatting.
- Scalable Architecture: Built-in support for high-availability deployments and enterprise-grade security.
Creating an MCP Server Using GSX
OneReach.ai GSX Agent Platform provides a visual, flow-based approach to building MCP servers through an intuitive drag-and-drop interface without writing code.
1. Add Set up MCP Server Gateway Step
Start by adding the Set up MCP Server Gateway step to your flow. This establishes your server’s public endpoint and defines its core metadata:
Server Configuration:
- Server ID: Choose a unique identifier that becomes your subdomain (e.g., weather-service)
- Server Name: A friendly name for identification
- Server Description: Brief explanation of the server’s purpose (e.g., “Weather lookup service”)
- Server Version: A version for tracking updates (e.g., “1.0.0”).
Once configured, this gateway step initializes your MCP Server endpoint and metadata. Your server URL becomes something like this: https://<Server-ID>.mcp.svc.staging.api.onereach.ai/mcp
2. Define Your Tools
Click Add Tool to create executable actions that your AI agents can invoke:
Tool Configuration:
- Tool Name: Unique identifier (e.g., get_weather)
- Tool Description: Clear explanation of the tool’s function (e.g., “Get weather details for a given city”)
- Input Parameters (JSON Schema): Definition for expected inputs. Example JSON Schema for a weather tool:
{
"type": "object",
"properties": {
"cityName": {
"type": "string"
}
},
"required": ["cityName"],
"additionalProperties": false
}3. Implement Tool Logic
Once defined, your tool creates an exit leg labeled Tool: get_weather. Connect this to your business logic:
- Read Parameters: Extract input values using GSX merge fields
- Execute Logic: Call external APIs, query databases, and perform calculations
- Return Response: Add a Send MCP Response step to package and return results.
The GSX flow automatically handles JSON-RPC (JavaScript Object Notation – Remote Procedure Call) formatting and error handling, ensuring your tool responses are properly structured for MCP clients.
4. Define Data Resources
Click Add Resource to create data access points:
Resource Configuration:
- Resource Name: Unique identifier (e.g., get_user_profile)
- Resource URI: Can be static or dynamic.
URI Examples:
- Static: user://default/profile
- Dynamic Template: user://{userId}/profile (clients substitute {userId} with actual values).
5. Implement Resource Logic
Similar to tools, resources create exit legs (e.g., Resource: get_user_profile) where you:
- Parse URI Parameters: Extract dynamic segments from the resource URI
- Fetch Data: Query your data sources
- Return Structured Data: Use Send MCP Response to return the resource content.
6. Create Prompt Templates
Click Add Prompt to define reusable instruction templates:
Prompt Configuration:
- Name: Unique identifier (e.g., help_get_weather)
- Description: Purpose explanation
- Messages: Define conversation templates with roles (Assistant/User) and content.
Important Note: Prompts are static templates that return exactly what you define — no dynamic data processing or code execution occurs during prompt invocation.
Security and Access Control
Your MCP server is set up to be publicly reachable by default — meaning anyone with the URL (e.g. https://<Server ID>.mcp.svc.staging.api.onereach.ai/mcp) could try to call it.
For production deployments, implement authorization controls:
- OAuth 2.0 Integration: Secure client authentication
- Dynamic Client Registration: Manage authorized applications
- Policy Enforcement: Control access to specific tools and resources.
Testing and Deployment
Verification Process: Once that’s working, your MCP server will be served at: https://<Server-ID>.mcp.svc.staging.api.onereach.ai/mcp
Testing Options:
- MCP Inspector: Developer tool for debugging and testing your server
- SSE Connection: Alternative endpoint for legacy connections at: https://<Server ID>.mcp.svc.staging.api.onereach.ai/sse.
MCP + GSX Security Guide
As we mentioned, by default, your MCP Server is publicly accessible, meaning anyone with the URL can invoke tools or read resources. To restrict access and implement role-based access control (RBAC), you can enable built-in authorization features in the Set up MCP Server gateway step.
1. Enable Bearer Token Validation
Turn on Validate Authorization: Bearer <token> to require all incoming requests to include a valid access token. This adds an Auth: Validate token leg to your flow, where you check the token’s validity. If valid, return HTTP 200 OK and allow the request; if missing or invalid, return HTTP 401 Unauthorized and reject it. This ensures only authorized clients can access your MCP server.
2. Enable OAuth Metadata Discovery
OAuth Authorization Server Metadata (also called OAuth Discovery) is a standardized JSON document published at a well-known endpoint (/.well-known/oauth-authorization-server). This document allows clients to automatically discover how to interact with your OAuth server. It follows the OAuth 2.0 Metadata specification (RFC 8414).
Enable OAuth Metadata Discovery to automatically expose the endpoint:
https://<server ID>.mcp.svc.staging.api.onereach.ai/.well-known/oauth-authorization-server.
Use Send MCP Response in that leg to return a JSON structure like this:
{
"issuer": "https://<server ID>.mcp.svc.staging.api.onereach.ai/",
"authorization_endpoint": "https://<server ID>.mcp.svc.staging.api.onereach.ai/authorize",
"token_endpoint": "https://<server ID>.mcp.svc.staging.api.onereach.ai/token",
"registration_endpoint": "https://<server ID>.mcp.svc.staging.api.onereach.ai/register",
"scopes_supported": ["read", "write", "openid", "email"],
"response_types_supported": ["code", "token"],
"grant_types_supported": ["authorization_code", "client_credentials", "refresh_token"],
"token_endpoint_auth_methods_supported": ["client_secret_post", "client_secret_basic"],
"code_challenge_methods_supported": ["plain", "S256"],
}3. Enable Standard Auth Endpoints
When you toggle “Enable Standard Auth Endpoints”, your MCP server activates built-in OAuth flows that handle the most common authentication scenarios:
- /register – Dynamic client registration
- /authorize – Authorization code flow
- /token – Token issuance
Together, these create the flow legs: Auth: handle /register, Auth: handle /authorize, and Auth: handle /token.
Here’s how each works in practice:
- /register: Allows dynamic client registration (per OAuth RFC 7591). Each client is issued unique credentials such as client_id and client_secret. In real-world deployments, this data should be stored in a secure, persistent location like a database or vault.
- /authorize: Manages user consent and issues short-lived authorization codes. These codes act as proof of consent and can later be exchanged for access tokens. Validation ensures only trusted clients receive codes, protecting against misuse.
- /token: Handles the secure exchange of authorization codes for tokens. If the request is valid, the server issues an access_token (and optionally a refresh_token); if not, the request is rejected. Proper storage ensures tokens can be revoked or verified later.
By enabling these endpoints, organizations ensure a secure, standards-based way for clients to authenticate, request consent, and obtain the tokens needed to interact safely with MCP servers.
Harnessing Agentic AI for Smarter Business Processes
IDC research shows that enterprise spending on AI is about to grow by nearly 32% year over year between 2025 and 2029, hitting $1.3 trillion. [2] What’s driving this surge? Agentic AI — systems that can manage teams of AI agents and handle complex, multi-step tasks without human-in-the-loop (HitL) intervention at every step. Simply put, enterprise IT is changing fast, and businesses are ditching basic chat interfaces for AI that actually gets stuff done.
OneReach.ai’s Generative Studio X (GSX), combined with MCP, makes it way easier to tap into this potential. By standardizing integrations, keeping security tight, and enabling low-code development, MCP and GSX let enterprises deploy AI agents that securely access live data, execute workflows, and scale across systems without breaking a sweat. These tools help organizations turn scattered AI tools into unified, production-ready solutions — making workflows and processes smarter, faster, and more reliable for teams across business, IT, and innovation functions.
