AI agents are evolving fast—from tools that generate content to systems that take real action across your enterprise. Model Context Protocol (MCP) is quickly emerging as the architectural standard that allows large language models (LLMs) to interact with enterprise systems in a safe, structured, and governed way.
But MCP isn’t plug-and-play. It demands foundational readiness, clear governance, and smart choices about ownership, implementation, and supervision. Before diving in, organizations must assess their AI architecture, security posture, and integration maturity. Here’s how to evaluate your readiness—and what to do if you’re not there yet.
When MCP Adds Value—and When It Doesn’t
An MCP server is a perfect fit for an agentic workflow where LLMs need to take actions rather than simply generate text from prompts. MCP servers allow the agents to access enterprise systems in a structured and secure manner.
Use MCP when
- You want agents to read and write data in enterprise systems
- You want the LLM to perform actions and not just answer questions
- Security and auditability are top concerns
- You need a consistent abstraction when integrating multiple systems
When MCP might not provide value
- Not all problems are best solved with AI, including Agentic AI. If you can automate workflows using simple, deterministic API calls that do not require AI reasoning, this is preferred.
- A prerequisite to Agentic AI is having a solid foundation of governed, scalable, iPaaS-designed integrations. If you have not yet built this foundation, our advice is to start there. Don’t deploy MCP servers in uncontrolled, unintegrated environments or where numerous point-to-point integrations have been built using various tools and methods. You need a solid foundation of connectivity between your systems. MCP servers also need to be governed, and building without this governance foundation creates additional risk and technical debt.
Pros and Cons of Building Your Own MCP Server
MCP is relatively simple to implement, and building your own server gives you complete control over your data, code, and security posture. You can tailor the server to your exact business needs, expose only the tools you require, and run it entirely inside your infrastructure for maximum control and compliance.
This approach is ideal when you want full ownership of the integration layer or need to meet strict regulatory or security requirements.
However, implementing your own MCP server does come with some cons. The drawback is that you now need the developer expertise to build and maintain it. You also have to manage everything yourself, including authentication, credential storage, logging, monitoring, policy enforcement, and scaling.
Building your own MCP server can be worthwhile for highly regulated organizations or when you need specialized services not available through off-the-shelf MCP offerings. However, it also means assuming the operational and security responsibilities that a managed platform would typically handle for you.
How to Secure Your MCP Implementation
MCP is powerful, but with great power comes great responsibility.
Do’s
- Use least-privilege service accounts when connecting to any target system. Grant only the permissions required for the specific tool, and expose only the necessary tools needed for the agent to perform the tasks assigned to them. Roles Based Access Control applies to Agents.
- Enforce rate limits and quotas to prevent accidental overload or abuse.
- Log every tool invocation, including parameters, calling identity, and timestamps.
- Log every policy violation and have a clear strategy for alerting, escalation, and resolution.
- Store credentials in a secure vault, never in plaintext configs.
- Maintain secure software development best practices. This includes having distinct MCP servers for development, testing, and production.
- Thoroughly test tools before enabling them in production, especially ones that modify data.
- Require human approval for mission-critical actions, such as financial updates, payroll changes, or irreversible operations.
- Restrict network access so the MCP server can only reach the systems it is supposed to connect to.
Dont’s
- Never give an LLM direct access to systems without strict tool boundaries and explicit permissions.
- Never expose admin or high-privilege credentials through an MCP server.
- Never deploy off-the-shelf or open-source MCP servers without a full security review, including code audit, permission scoping, and compliance checks.
- Never expose tools broadly — always limit which LLMs or agents can call which tools.
Techniques to Reduce LLM Hallucinations and Improve Output Consistency
LLMs have a precision and accuracy problem. We have all heard about these problems – AIs can hallucinate, generate significantly different results with minor changes in prompts, and even struggle with simple tasks like counting.
These problems are very significant in an enterprise context. However, there are techniques to minimize the probability and impact of these issues occurring. Some best practices to consider include:
- Don’t ask an AI to do any task that can be solved using deterministic methods. Not only is this computationally expensive (measured in terms of token consumption), but it also introduces variability and risk into processes where none should exist.
- You can leverage Agents through MCP servers to take deterministic action. For example, instead of asking the LLM to count, you can expose a tool or skill in the MCP server to do the counting using standard methods.
- Limit the functionality of the tool or skill exposed in the MCP server to the minimum necessary for the agent to perform its action. For example, don’t expose a skill to write to a system if you only want the agent to read.
- Build supervision methods in agentic workflows (where the supervisor can be human, algorithmic, or agentic) that monitor outputs from agentic workflows and trigger alerts and other actions if anomalies occur.
- While we are in an era of “vibe coding,” having a solid architectural framework that incorporates best practices in security, compliance, and quality assurance is indispensable.
MCP opens the door to a new era of intelligent enterprise orchestration, but only if your systems, governance, and strategy are ready for AI agents. Before you implement MCP, take a step back and assess your integration maturity, risk posture, and the outcomes you want AI agents to drive. If you treat MCP like just another connector, you’ll miss its potential. At Dispatch, our successful client collaborations treat MCP like a strategic layer in order to deliver on the enterprise value promised by AI agents.
Irfan Patel is a Principal Consultant at Dispatch Integration, bringing over eight years of experience delivering complex HR and enterprise integration solutions. With a background spanning senior integration consulting and HR solutions development, Irfan specializes in designing and leading scalable integrations that align people, processes, and technology. He has deep expertise in translating HR system requirements into effective, reliable integration architectures and is known for guiding clients through technically complex initiatives with clarity and precision.
