What is the Model Context Protocol?
Quick definition
The Model Context Protocol (MCP) is an open standard that simplifies how AI applications connect to external tools and data. Instead of building custom connectors for every model or assistant, you expose capabilities once via an MCP server. Any compliant client—whether it’s an LLM, AI agent, or internal app—can then discover and use those capabilities with consistent security, logging, and governance.
Think of MCP as USB-C for AI: one port, many devices, less hassle.
Why MCP beats ad hoc connectors and function calling
- Ad hoc connectors require custom integration for each model or assistant, making them slow to build and hard to maintain.
- Function calling is tied to specific models and vendors, with inconsistent formats and authentication patterns.
MCP solves these problems by standardizing access. Tools, resources, and prompts are described once on the server. Any MCP-compliant client can discover and call them the same way. This reduces duplicate work and avoids vendor lock-in.
Core concepts at a glance
- Client: the entity requesting work—like a desktop assistant, ChatGPT, Claude, or an internal app
- Server: the system that exposes tools, resources, and prompts in a standard format
- Tools: actions the client can invoke (e.g., run SQL, call an API, open a ticket)
- Resources: readable context such as documents, files, knowledge bases, or vector search results
- Prompts: reusable templates with parameters for consistent task framing
- Transport: a simple, language-agnostic JSON-RPC–style exchange
How MCP operates in practice
At its core, MCP enables AI clients to interact with tools and data through a standardized server interface. You deploy an MCP server alongside trusted systems—such as your data warehouse, ticketing platform, or internal APIs—and expose capabilities like tools, resources, and prompts. Any compliant client can discover and use these capabilities, with permissions and controls defined by you.
The operational loop
Once connected, the client follows a structured loop to complete tasks:
- Perceive the task and fetch relevant context (resources).
- Plan the next step and select an allowed tool.
- Call the tool with parameters and credentials.
- Evaluate the result against rules or policies.
- Decide what to do next based on the result.
- Repeat until the task is complete, with optional human approval gates.
This loop supports both autonomous agents and human-in-the-loop workflows, making it flexible for a range of enterprise scenarios. Autonomous agents can make decisions and take actions independently based on their programming, training, or learned behavior. Human-in-the-loop workflows intentionally include human oversight or decision-making at key stages of a process.
Where MCP servers run
MCP servers can be deployed in various environments depending on the use case:
- Locally, for personal productivity assistants.
- Inside your network, as microservices exposing internal APIs securely.
- At the cloud edge, to consolidate access and enforce auditing across systems.
What MCP makes possible
By standardizing how AI clients access tools and data, MCP unlocks a range of capabilities that improve reliability, reduce integration overhead, and support scalable AI deployment across the enterprise.
MCP enables:
- Reliable tool use across models
A single MCP server can support multiple clients—like a desktop assistant, a customer-support copilot, and a batch automation—without rebuilding integrations. This flexibility allows teams to switch or combine LLMs as needed. - Shared, consistent context
Standardized resources such as documents, embeddings, and dashboards ensure that assistants retrieve the same trusted materials. This reduces hallucinations and keeps outputs grounded in enterprise facts. - Fewer bespoke connectors
Instead of maintaining dozens of glue scripts, engineering teams manage one well-instrumented server. Credentials rotate in one place, and all activity flows through a unified audit path.
These capabilities make MCP especially valuable in environments where governance, interoperability, and cost control are critical.
Representative applications
MCP supports a wide range of use cases, including:
- Data access: A read-first SQL tool retrieves governed data for analysis. Write actions can be added later with change approvals.
- Knowledge and search: Vector or enterprise search tools return cited passages for answers or draft generation.
- Ops and workflows: Ticketing, messaging, and runbook tools operate with built-in limits and rollback safeguards.
- Multi-agent flows: A planner agent invokes MCP tools, a QA agent verifies results, and a supervisor approves irreversible actions.
Why MCP works for the enterprise
MCP isn’t just a technical convenience—it’s a strategic enabler for enterprise AI. By standardizing how tools and data are exposed to AI clients, MCP helps organizations move faster, govern more effectively, and reduce operational overhead.
Here’s what it delivers:
- Interoperability and speed
With reusable tool schemas and a consistent interface, new assistants and use cases can be onboarded quickly. You don’t need to re-implement logic for every client or model. - Governance you can prove
Every tool call is traceable—inputs, outputs, cost, and result codes are logged. You control which tools exist, who can call them, and how they’re parameterized, making compliance and audit readiness much easier. - Lower cost and higher reliability
Shared adapters reduce integration sprawl. Instead of managing dozens of fragile endpoints, you maintain fewer, well-instrumented ones. When something fails—whether due to auth, timeout, or validation—you get clearer error classes and faster resolution.
These benefits make MCP a strong fit for organizations that need to scale AI responsibly, without sacrificing control or efficiency.
Security and governance that scales
MCP is designed with enterprise-grade security and governance in mind. It treats tool access like any other production system—requiring tight controls, full observability, and rollback mechanisms for sensitive actions.
Key practices include:
- Identity and least privilege
Avoid broad “super tokens.” Assign short-lived, scoped credentials per tool and per task. Secrets stay server-side and rotate on a schedule. - Network and data boundaries
Run the server in environments where you can enforce egress rules and validate inputs and outputs. Start with read-only access; allow writes only with constraints and approvals. - Approvals and rollback
For irreversible actions—like moving money, updating PII, or changing configurations—require human sign-off. MCP can package evidence automatically and support one-click rollback. - Observability and audit
Every call is traceable: who invoked it, what parameters were used, what response was returned, and how long it took. These traces feed dashboards for reliability, security, and finance teams. - Patching and change control
Treat servers, tools, prompts, and policies as versioned artifacts. Patch quickly, review changes, and maintain a clear audit trail.
These controls help ensure that MCP deployments meet enterprise standards for safety, accountability, and operational resilience.
MCP in the broader ecosystem
MCP is gaining traction across the AI landscape as a flexible, open standard for tool and data access. Its growing ecosystem makes it easier for enterprises to adopt MCP without vendor lock-in or custom engineering.
Here’s what’s happening:
- Open specification and launch
MCP is built as a collaborative spec with reference implementations. Community momentum is strong, with contributions from multiple vendors and frameworks. - LLM client support
Major assistants and agentic frameworks are adding native support for MCP, allowing them to discover and call tools exposed by MCP servers without custom integration. - Adapters and orchestration frameworks
Graph-based and agentic frameworks are publishing adapters that treat MCP tools as first-class citizens—making it easier to build multi-step workflows and agentic flows. - Enterprise guidance
Cloud and security vendors are releasing architecture patterns and best practices for safe, scalable MCP adoption. This includes recommendations for network boundaries, credential management, and audit logging.
Together, these developments make MCP a practical and future-proof choice for enterprises looking to standardize AI integration across teams, tools, and platforms.
Getting started with MCP: a 6-step plan
Rolling out MCP doesn’t require a massive overhaul. Most teams begin with a single workflow, a few tools, and a tightly scoped server. Here’s a practical path to get started:
- Pick one workflow.
Choose a bounded process with measurable pain—something like “pull case facts and propose a disposition” or “run read-only SQL to assemble weekly KPIs.” List the exact data and actions required. - Stand up a minimal server.
Deploy an MCP server close to your systems. Define 2–3 tools with clear schemas (inputs, outputs, limits) and 1–2 resources (documents, search). Keep permissions tight—read-only to start. - Connect a client and test happy paths.
Wire up a desktop assistant, internal copilot, or your own app. Run end-to-end calls with realistic payloads and capture traces for each run. - Add observability, budgets, and approvals.
Turn on request/response logging, latency and error metrics, and cost tracking per task. Set per-tool budgets and rate limits. Require approvals for risky actions. - Run a pilot with a small cohort.
Select a team, run real work for two to four weeks, and compare KPIs to a control period. Gather qualitative feedback on speed, accuracy, and trust. - Harden and scale.
Introduce scoped write tools with rollback, rotate credentials automatically, document runbooks, and publish a change-control process. Add more clients without redoing the server.
This phased approach helps teams validate MCP in a controlled setting before expanding to broader use cases.
KPI scorecard: what to measure from day one
To ensure MCP delivers value and remains safe at scale, teams should track a core set of performance and reliability metrics from the start. These KPIs help validate early pilots, guide optimization, and support governance.
Key metrics include:
- Tool-call success rate: Percentage of completed calls versus attempted calls. A high success rate indicates stable integration and correct usage.
- Time to first successful call: How long it takes to get a green run in production. Useful for tracking onboarding speed and setup efficiency.
- p95 latency: The 95th-percentile latency per tool. Helps protect SLAs and identify performance bottlenecks.
- Error distribution: Categorizes failures—authentication issues, timeouts, validation errors, upstream faults—to guide debugging and reliability improvements.
- Cost per completed task: Tracks compute, token usage, and invocation costs per workflow. Useful for budgeting and cost control.
- Escalation rate: Percentage of runs that require human intervention. Helps assess trust, automation quality, and edge-case handling.
- Incident count: Number of blocked or rolled-back actions due to policy violations or system errors. A key signal for governance and safety.
Build it on Teradata: Enterprise integration made easy
Teradata provides the infrastructure to make MCP reliable, scalable, and deeply integrated with your enterprise data stack. If you're already running analytics on Teradata, you have a head start—your governed data, vector search, and model operations are ready to plug into MCP.
Here’s how Teradata supports each layer:
- Expose governed data with Teradata’s MCP Server.
Publish a read-first SQL tool that targets Teradata VantageCloud Lake, using scoped schemas and row-level policies. Once trust and rollback paths are in place, you can expand to controlled write actions. - Ground answers with Enterprise Vector Store.
Register Teradata’s vector store as an MCP resource for retrieval and grounding. This ensures assistants cite a single source of truth—not scattered or stale copies. - Operate with ClearScape Analytics® ModelOps.
Treat servers, tools, prompts, and policies like code. Version them, require approvals, monitor runs, and audit changes. If something drifts, roll back cleanly and confidently.
This integration stack helps teams move from prototype to production with governance, observability, and flexibility built in.
Conclusion and next steps
MCP gives enterprises a standardized, secure way to expose trusted tools and data to AI assistants—without the complexity of custom connectors or fragmented governance. It’s fast to implement, easy to scale, and built for real-world reliability. It also serves as a bridge between AI assistants, enabling seamless integration with platforms, including Teradata applications.
The payoff:
- Speed: fewer bespoke integrations, faster time to value
- Governance: centralized control, auditability, and rollback
- Efficiency: lower total cost to integrate and maintain
Ready to move forward?
- Schedule an MCP design session with Teradata.
Identify the right pilot workflow, define tool schemas and data scopes on VantageCloud Lake, and align KPIs like success rate, p95 latency, and cost per task. - See a live demo.
Watch an agentic workflow call Teradata SQL and Enterprise Vector Store through an MCP server—with full trace, approvals, and rollback under ClearScape Analytics® ModelOps.