What is an MCP server? The complete guide

Who would have imagined that when Anthropic released the Model Context Protocol (MCP) specification in November 2024, it would have landed with so much force! Almost a year and a half later, MCP servers are everywhere.

All of the largest AI companies support MCPs. OpenAI supports them. Google DeepMind supports them. The Linux Foundation houses the spec. And enterprise testing platforms like Tricentis have shipped production MCP servers across their entire product suite.

What happened between the release and now is worth understanding, especially if you’re responsible for testing, integration, or automation in a complex software environment.

An MCP server is a lightweight application that exposes data, tools, and functionality to AI models through a standardized protocol called the Model Context Protocol (MCP). 

It acts as the bridge between an AI system—like an LLM-powered assistant—and the external services that system needs to interact with: databases, APIs, testing platforms, project management tools, and more.

If you’ve spent any time wrestling with custom API integrations between AI tools and your existing stack, MCP servers exist to make that problem go away.

How does an MCP server work?

TL;DR: MCP follows a host–client–server architecture using JSON-RPC. The server exposes capabilities (data and actions), the client manages communication, and the AI host interacts with everything in a stateful, context-aware way.

For anyone who has built or consumed web APIs, MCPs will give a similar feeling. MCP follows a similar client-server architecture, though the details differ in important ways.

The protocol defines three core components:

1. Host

This is a user-facing application, such as an IDE like Cursor, an AI assistant like Claude or ChatGPT, or a custom-built tool. The host is what a human interacts with.

2. Client

This would be an intermediary that lives inside the host. It manages connections, routes requests, and handles protocol-level concerns like authentication and capability negotiation.

3. Server

This is a standalone program that provides capabilities like data to read, functions to call, or prompts to follow. This is the MCP server itself.

Communication between client and server uses JSON-RPC 2.0, a lightweight remote procedure call protocol that maintains state across requests.

Unlike a stateless REST call, the AI can carry context through a multi-step conversation with the server. This statefulness is what separates MCP from simply bolting an LLM onto a traditional API.

MCP also happens to be transport agnostic, which is a fancy way of saying it doesn’t care how the client and server talk to each other, as long as they follow the protocol.

When everything runs on the same machine, communication happens over standard input/output—fast, simple, no network overhead.

Also, when the server lives somewhere else (which is increasingly the norm for enterprise deployments), MCP uses HTTP with Server-Sent Events, so the server can stream updates back to the client as work progresses rather than forcing it to poll for results.

When everything runs on the same machine, communication happens over standard input/output—fast, simple, no network overhead.

MCP server capabilities

TL;DR: MCP servers expose four capability types—Resources (read data), Tools (execute actions), Prompts (guide interactions), and Sampling (request structured reasoning)—all in a self-describing format AI models can automatically understand.

MCP servers aren’t limited to a single type of interaction. The protocol defines four distinct capability categories, and a single server can offer any combination of them.

1. Resources

Resources are passive data that the AI can read, such as file contents, database schemas, API response structures, or configuration details. They give the AI context about the environment it’s working in.

2. Tools

Tools are callable functions. This is where the real action happens. A tool might trigger a test run, create a defect ticket, generate a report, or deploy a mock API.

Tools are defined with names, descriptions, and structured input/output schemas so that the AI understands what each one does and how to use it.

3. Prompts

Prompts are reusable message templates that guide how the AI interacts with the server. They help ensure consistent, predictable behavior across sessions and users.

4. Sampling

Sampling is a more advanced capability where the server can request agent-like reasoning from the AI itself. This is essentially asking the model to think through a problem before responding.

An MCP server’s capabilities are self-describing, meaning any connected AI client can discover and understand them without prior knowledge of the server’s API. 

This is what makes the protocol genuinely universal rather than just another integration standard with a spec nobody reads.

Benefits of an MCP server

TL;DR: MCP eliminates custom integrations, lowers technical barriers, enforces security through existing permissions, and future-proofs AI integrations by staying model-agnostic.

We’ve read about how an MCP server works and its capabilities. Let’s take a look at the value proposition of an MCP server:

Before MCP, connecting an AI model to an external tool meant building a bespoke integration.

1. Standardization eliminates integration sprawl

Before MCP, connecting an AI model to an external tool meant building a bespoke integration. Something specific for each tool, each model, each use case. MCP collapses that N×M problem into a single protocol. Just build one MCP server, and any MCP-compatible AI client can use it.

2. Accessibility drops the technical barrier

Dave Colwell, VP of AI and ML at Tricentis, described this shift in a recent blog post: “You can simply provide a prompt saying ‘this is what I want to achieve,’ and the AI will propose a set of actions that it can take that you can review.”

Less reliance on UI expertise or scripting knowledge means more people on the team contributing to quality.

3. Security is built into the architecture

MCP servers support user-scoped authentication, so access controls follow the same permissions model your tools already enforce. Remote MCP deployments don’t require local installation, which reduces surface area for supply-chain risks.

4. Future-proofing is a real outcome, not just marketing copy

Since MCP is model-agnostic, your server works with whatever AI system your organization adopts next—Claude, ChatGPT, an open-source model, or something that doesn’t exist yet.

When Anthropic donated MCP to the Linux Foundation’s Agentic AI Foundation in December 2025, it signaled that the protocol’s governance would remain vendor-neutral for the long haul.

MCP servers thus help reduce integration costs, expand who can use enterprise tools, and make infrastructure AI-ready without lock-in.

MCP server examples

TL;DR: The MCP ecosystem includes both open-source community servers and enterprise implementations like Tricentis, which provide AI-driven access to testing, performance, and coverage tools.

The MCP ecosystem has also grown remarkably since Anthropic open-sourced the specification. Multiple community-built servers exist for everything from GitHub repository access to Slack messaging to database queries.

For instance, within enterprise software testing, Tricentis has released production MCP servers across four cloud products:

1. Tricentis Tosca Cloud

Tricentis Tosca Cloud provides the ability to automate test design, execution, and validation through AI prompts.

This also facilitates actions like scaffolding test cases from natural-language descriptions, and the MCP server handles creating the folder structure, suggesting existing modules, and placing everything in the right location.

It also supports creating API simulations, which lets teams validate integration points before the real service is available, thereby enabling shift-left testing.

2. Tricentis qTest SaaS

Tricentis qTest SaaS helps in managing test case structure, execution, defect creation, and requirements traceability directly from AI assistants like Claude, Cursor, or ChatGPT.

The practical effect is that someone can prompt an assistant to pull up all test cases linked to a specific requirement, review recent execution logs, generate defect tickets from failures, and link everything together without having to navigate the qTest UI at all.

3. Tricentis NeoLoad Web

Tricentis NeoLoad Web provides the option to enable natural-language interaction with performance testing workflows, from launching tests to monitoring execution in real time, analyzing results, and producing detailed reports.

This makes performance testing more accessible to team members who need results but aren’t performance engineering specialists.

4. Tricentis SeaLights

Tricentis SeaLights provides AI access to test coverage analytics at a remarkably granular level, helping teams identify untested code changes and build intelligent test execution plans that skip what’s redundant, and surface gaps before a release, thereby turning coverage from a dashboard metric into an actionable part of the development workflow.

All four are available remotely with no local installation required. Teams owning valid product licenses can connect immediately at no additional cost for the protocol layer.

Beyond Tricentis, the open-source MCP ecosystem includes servers for file system access, web scraping, database interaction (PostgreSQL, MySQL, SQLite), and cloud platform management. The official MCP documentation maintains a growing directory of community-contributed servers.

MCP server use cases

TL;DR: MCP enables AI-assisted test case generation, performance test orchestration, defect triage, and coverage analysis—all through natural-language interaction instead of manual UI navigation.

The flexibility of MCP servers means they show up across a range of workflows. Here are some of the patterns that deliver the most value for testing and quality engineering teams.

1. Automated test case generation

One can describe a user flow in plain language and let the AI generate structured test cases, place them in the correct project hierarchy, and link them to requirements.

This opens up testing to folks without traditional engineering experience and speeds up test suite creation, thus promoting more reliability in your software.

2. Performance test orchestration

Instead of navigating a specialized UI to configure load scenarios, trigger runs, and export results, a team member can prompt an AI assistant connected to a NeoLoad MCP server to handle the entire workflow conversationally.

That lowers the barrier enough for engineers who don’t live in the tool every day to run meaningful performance tests on their own.

3. Defect triage from test results

After a test run surfaces failures, an AI tool connected to a qTest MCP server can analyze the results, identify failure patterns, generate properly categorized defect tickets, and link them back to the original test logs—work that typically takes hours of manual correlation and rarely gets done with consistent naming or linking conventions.

4. Test coverage analysis

An AI connected to something like SeaLights can evaluate which code changes lack test coverage, recommend which tests to run (and which to skip), and surface coverage gaps before a release.

That turns coverage analysis from a periodic audit into a continuous, low-effort part of the workflow.

MCP server tools

TL;DR: MCP servers connect to AI assistants (Claude, ChatGPT), IDEs (VS Code, Cursor), and custom agents, allowing teams to integrate AI into existing workflows without bespoke API integrations.

MCP servers are designed to work with the AI assistants and development environments that teams already use. Thus, the compatibility spans a broad range of clients:

1. AI assistants: Claude (Anthropic), ChatGPT (OpenAI), GitHub Copilot, and custom LLM implementations all support MCP connectivity.
2. Development environments: Cursor, Visual Studio Code, and other IDEs with MCP client support can connect to servers directly from the developer’s workflow.
3. Custom agents: Organizations building proprietary AI tooling can register MCP servers as part of their agents’ available toolkit, using official SDKs in Python, TypeScript, Java, and Kotlin.

Tricentis products like Tosca provide pre-built MCP tools that facilitate discrete actions like “scaffold test case,” “run regression suite,” or “create API simulation,” and require zero custom development.

The full list of available MCP actions for Tosca Cloud is published in the Tricentis documentation.

For teams evaluating MCP tooling, the key question isn’t whether your AI client supports MCP—most do, or will shortly. The question is whether the servers you need expose the right capabilities for your workflows.

MCP servers are designed to work with the AI assistants and development environments that teams already use.

MCP server pricing

TL;DR: The MCP protocol is free and open source. Enterprise MCP servers are typically bundled with existing product licenses; the primary cost consideration is AI model usage (API calls and tokens).

The MCP specification is open source and free to implement. Additionally, there is no licensing cost for the protocol itself. Anthropic donated the standard to the Linux Foundation’s Agentic AI Foundation in late 2025, ensuring its governance remains vendor-neutral.

For enterprise MCP servers, pricing is typically tied to the underlying product license rather than the MCP layer. 

Community-built and open-source MCP servers are generally free. Organizations building custom servers for internal tools incur standard development costs but no protocol licensing fees.

Companies like Tricentis provide their MCP servers at no additional cost for customers with valid licenses. There is no separate MCP subscription.

The practical pricing consideration isn’t usually the MCP server itself—it’s AI client usage. LLM API calls (to Claude, ChatGPT, and similar services) carry their own costs, which scale with usage volume and model selection.

Teams planning for production MCP workflows should factor in those per-token or per-request costs alongside server hosting.

An MCP server exposes capabilities, whereas an agentic AI system decides which capabilities to use, in what order, and toward what goal.

How agentic AI enhances MCP testing

TL;DR: MCP provides structured access to tools; agentic AI decides how to use them. Together, they enable semi-autonomous, closed-loop testing workflows with human oversight.

MCP servers and agentic AI are natural partners, but they solve different parts of the problem.

An MCP server exposes capabilities, whereas an agentic AI system decides which capabilities to use, in what order, and toward what goal.

Combine the two and you get something qualitatively different from either piece alone: an autonomous testing workflow that can reason through multi-step processes without step-by-step human instruction.

Let’s paint a picture: An agentic AI receives a Jira ticket describing a new feature.

Connected to MCP servers for Tosca and qTest, the agent could autonomously generate test cases from the ticket’s acceptance criteria, scaffold them in the correct project structure, execute the suite, analyze the results, and file defects for any failures—all from the initial prompt.

Tricentis has been building toward exactly this with its agentic test automation capabilities in Tosca, which complement MCP by handling test case authoring through natural language while MCP handles execution, analysis, and reporting.

Together, they form what Tricentis describes as a closed-loop AI testing system: author, execute, analyze, improve.

An important nuance worth flagging is that agentic doesn’t mean unsupervised. These systems propose actions for human review, maintain audit trails, and operate within the permission boundaries enforced by the MCP server’s authentication layer.

The goal is to remove toil, not remove h2oversight.

For QA leaders evaluating this space, the question has shifted from “should we adopt AI in testing?” to “how do we give AI structured, secure access to our testing infrastructure?” MCP servers answer that second question. Agentic AI answers the third: “And then what does the AI actually do with that access?”

See how Tricentis enables AI-driven testing across your quality engineering workflow.

This post was written by Deboshree Banerjee. Deboshree is a backend software engineer with a love for all things reading and writing. She finds distributed systems extremely fascinating and thus her love for technology never ceases.