How to Build and Use MCP Servers for Secure AI Access

What’s an MCP server and what’s it for? 

Quick definition 

An MCP server is a lightweight program that gives AI applications a standardized, secure way to act on real systems (such as calling APIs, querying databases, or reading files) via the Model Context Protocol. Instead of building custom plugins, you expose clearly defined tools and resources that any compatible client can discover and invoke.

How MCP servers work 

An AI client connects to the server and asks, “What can you do?” The server responds by advertising its capabilities:

• Tools: actions like “create_ticket” or “query_vantage”
• Resources: read-only references like a data dictionary or policy documents 

The client then invokes a tool with structured inputs. The server performs the action, handles authentication, and returns a structured result. Because requests follow a consistent interface, teams can swap clients or reuse servers without rewriting glue code.

Why choose MCP? 

By adopting a well-structured MCP server, teams can replace many ad-hoc integrations with a reusable, secure, and governable interface for AI applications. Developers benefit from faster delivery, since tools are simple functions with predictable inputs and outputs. Platform teams gain traceability and control, with identity, scopes, logging, and change management that align with existing services.

Use MCP when an AI app needs to act on live systems or access governed data. Pair it with RAG (retrieval-augmented generation) for long, citable content, and consider fine-tuning when a task is repeated at high scale with strict latency or cost requirements.

MCP design and architecture 

How to build an MCP server step by step 

1. Scaffold: Start from an SDK or template. Name your server, add metadata, and enable health and introspection endpoints. 
2. Define your first tool: Choose one specific action that delivers clear value, such as querying a governed table, opening a ticket, or pulling an order status. Clearly define the inputs, outputs, and error formats from the start. 
3. Add a resource: Provide read-only context the client can reference—like API documentation, a data dictionary, policy snippets, or a sample SQL schema. 
4. Run locally and connect a client: Use your preferred client to verify tool discovery and make a basic call. Return a predictable payload (typically JSON) with fields the client can render without guesswork.  
5. Harden: Replace static tokens with short-lived credentials or workload identity. Store secrets in a vault and scope tools to the principle of least privilege. Add input validation, rate limiting, and audit logging. 
6. Deploy: Containerize and run behind your gateway with OIDC or mTLS. Register the server in your internal catalog so other teams can discover it. Define basic service-level objectives (SLOs) such as availability, p95 latency, and error rate, and configure alerts. 
7. Document: Provide a concise README that lists tools, parameters, example calls, and permission requirements. Include a sample client configuration to help others get started quickly. 

Before rollout, build a small suite of real tasks to validate your server’s behavior. Test for accuracy, latency, and failure handling across both expected and edge cases. Include scenarios like expired tokens, invalid inputs, and permission errors to ensure the server responds predictably and securely.

Documenting and sharing your MCP server 

Once your MCP server is up and running, it’s worth investing in clear, helpful documentation—especially if others will use or evaluate it. Start with a concise definition and a simple diagram showing how the client, server, tools, and data interact. Include a minimal quickstart that creates one tool and one resource, connects a client, and runs a real task. Add a short, scannable section that explains when to use MCP versus RAG or fine-tuning. To make the server feel real and relevant, describe one or two practical scenarios with realistic inputs and outputs. Finally, close with a checklist for securing and operating the server, and link to example repositories or templates that others can build from.

Architecture at a glance 

• Tools (actions): These are the verbs your server performs. Keep them small, composable, and scoped to specific permissions. 
• Resources (read only): These include the documents or metadata the client can reference for grounding or context. 
• Prompts (templates/macros): These are optional helper templates that standardize how a tool is called, making client interactions more consistent. 
• Observability features: These include request and response logs, success and error codes, latency metrics, and token usage where applicable. 
• Security envelope: This encompasses identity management (OIDC or workload identity), secret handling, scoped permissions, rate limiting, and network boundaries. 

Best practices  

Security, governance, and operations 

Treat an MCP server like any other production microservice:

• Use short-lived credentials and never embed secrets into code or prompts.  
• Enforce the principle of least privilege for every tool, ensuring the system fails securely if a permission error occurs. 
• Log requests with minimal but traceable metadata, such as who called what, when, and with which tool, and then correlate logs to user or service identity.  
• Version tools and prompts, run pre-production evaluations, and maintain a clear change log.  
• Define SLOs for availability and p95 latency; keep a lightweight runbook for incident response (e.g., rotating secrets, disabling tools, or rolling back a release). 

Common pitfalls 

Be especially mindful of common pitfalls that can undermine security and reliability. These include over-permissive scopes, hardcoded secrets, missing audit logs, and brittle error messages that clients can’t interpret. Avoid building a “God server” that tries to do everything—instead, keep tools focused, composable, and permission-scoped.

Selecting or reusing servers 

You don’t have to build everything. Reuse existing servers when they already expose the actions you need and support the right authentication model and SLAs. Build your own when you require custom actions, stricter governance, or deep integration with internal systems. Evaluate third-party servers based on client compatibility, supported authentication flows, logging and metrics, permission model and rate limiting, error handling, documentation quality, and vendor support. 

Practical use cases 

• Analytics assistants: Expose a query_vantage tool and a data_dictionary resource. The client converts a natural-language request into validated SQL, executes it via the tool, and returns results along with a brief rationale.
• Support triage: Provide tools to search tickets, summarize threads, and open or update cases using consistent fields. Include a resource that defines severity levels and escalation paths. 
• Document operations: Offer a compare_contracts tool that highlights differences against a template, along with a resource containing clause guidelines. Return a structured “findings” object for downstream workflow. 
• Operations automation: Wrap common tasks—such as restarting a job, checking a pipeline, or rotating a key—behind guarded, rate-limited tools with clear approval requirements. 

How Teradata helps 

If you already run analytics on Teradata, you can build useful servers quickly and safely.

• Teradata MCP Server – Community Edition: an open-source framework that gives AI agents transparent, trusted access to governed enterprise data and operational tools 
• Teradata VantageCloud Lake: a scalable platform for storing governed datasets that your tools can query, along with curated resources like data dictionaries and policy documents 
• Enterprise Vector Store: a retrieval system that surfaces the right snippets or examples when a tool needs document grounding or many-shot prompting 
• ClearScape Analytics® ModelOps: a suite for evaluating tools and agents, track latency, cost, and success rates, enforcing guardrails, and promoting changes with confidence 
• ClearScape Analytics Bring Your Own LLM: flexibility to choose the right model for each workflow—using long-context models where needed and smaller, faster models where latency and cost are critical—without vendor lock-in 

Conclusion 

MCP servers give AI applications a standardized, secure way to perform real work on real systems—safely and at scale. The best way to start is small: scaffold a server, add one high-value tool and one resource, connect a client, and run a single end-to-end task. Build it right from the beginning with strong identity management, secret handling, least-privilege scopes, and audit logging. Measure accuracy and latency, and refine based on real usage. As your case matures, you can decide whether to extend it with RAG or fine-tuning.

When you’re ready to move beyond the pilot phrase, Teradata’s MCP Server—Community Edition, combined with VantageCloud Lake, Enterprise Vector Store, and ClearScape Analytics ModelOps, provides a reliable foundation for turning that first tool into a governed, production-grade capability your teams can trust. Learn more or view a live demo.