I have been exploring around creating a few MCP servers for ServiceNow and. The experience has been interesting because MCP makes it much easier for AI assistants to interact with business systems without needing custom integrations for every use case.
In this article, I'll share what MCP is, why it matters, and how I used it to connect AI with ServiceNow.
What is MCP?
MCP stands for Model Context Protocol. It is an open protocol that provides a standard way for AI models to communicate with external tools, databases, APIs, and enterprise applications.
Think of MCP as a bridge between an AI assistant and the systems your organization already uses.
Without MCP, every AI application would need custom integrations for each tool. With MCP, developers can create MCP servers that expose capabilities in a consistent way, allowing AI clients to discover and use them.
In simple terms:
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AI asks for information or an action.
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MCP server receives the request.
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MCP server talks to the target system.
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Results are returned back to the AI.
This creates a simple and standardized workflow for connecting AI to business applications.
Why MCP Matters
One of the biggest challenges with AI is that models don't automatically have access to company data or business systems.
For example, an AI assistant cannot directly:
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Retrieve incidents from ServiceNow
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Create change requests
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Look up user records
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Query CMDB data
unless those capabilities are explicitly provided. This is where MCP becomes valuable.
Instead of building separate integrations for every AI application, organizations can create MCP servers that expose these operations once and make them available to any MCP-compatible client.
Some benefits include:
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Standardized integrations
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Easier tool discovery
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Better security controls
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Reusable implementations
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Faster AI adoption across enterprise systems
Building MCP Servers for ServiceNow
To better understand how MCP works, I created a few MCP servers for ServiceNow.
These servers expose ServiceNow data and operations as tools that AI assistants can use through the MCP protocol.
Example MCP Server
The server provides tools that allow AI assistants to interact with ServiceNow records without directly calling APIs themselves.
For example, the MCP server can:
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Retrieve incidents
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Search incidents
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Get incident details
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Query records from tables
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Retrieve user information
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Access CMDB data
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Perform other ServiceNow operations
The AI only needs to know which tool to use. The MCP server handles authentication, API calls, and response formatting. (Obviously we set up the auth method initially)
Example: Querying the Incident Table
One practical example is retrieving incident information from ServiceNow.
A user can ask:
To run an analysis in incident table.
The AI sends the request to the MCP server, which queries the ServiceNow incident table and returns the results.
Sample Query
Response
The response can include details such as:
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Incident Number
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Short Description
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Priority
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State
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Assignment Group
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Created Date
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Assigned To
This allows users to get information using natural language instead of manually navigating through multiple screens.
How MCP Changes the User Experience
Traditionally, users need to:
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Open ServiceNow
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Navigate to the correct module
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Apply filters
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Search for records
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Review results
With MCP-enabled AI, the workflow becomes much simpler:
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Ask a question in plain English
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AI selects the appropriate tool
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MCP server retrieves the data
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Results are returned instantly
The user focuses on the question instead of the process.
Challenges and Learnings
While building these MCP servers, a few things became clear:
- Security is Important
- Enterprise systems contain sensitive data. MCP servers should implement proper authentication, authorization, and access controls.
Tool Design Matters
- The quality of tool definitions has a direct impact on how effectively AI can use them. Clear descriptions and well-structured inputs make a big difference.
Keep Responses Simple
- Returning only the information users actually need helps improve the overall experience and reduces unnecessary context.
Final Thoughts
MCP is one of the most important standards for connecting AI with real-world applications.
Building MCP servers for ServiceNow gave me a practical understanding of how AI can move beyond and start interacting with enterprise systems in a meaningful way.
Instead of creating custom integrations for every AI application, MCP provides a common language that allows tools, systems, and AI assistants to work together.
As more organizations adopt AI, standards like MCP will play a key role in making enterprise integrations simpler, reusable, and easier to scale.
I'm continuing to experiment with additional ServiceNow MCP servers and new use cases, and it's exciting to see how quickly AI-powered workflows can be built once the foundation is in place.
Comment down you experiences using MCPS.
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