Creating MCP Servers for Building AI Agents

AI was once limited to internal pilots—impressive in demos, but rarely tied to measurable business outcomes. That’s changed. Today, AI systems are being integrated into workflows that impact decisions, operations, and outcomes.

That’s where the Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication come in. MCP provides a minimal interface for tool access and execution context. When paired with agent logic and A2A communication, it enables agents to reason and coordinate actions collaboratively.

This article explains what an MCP server is, why it matters for enterprise AI, and which capabilities to prioritize for scalable automation.

Why MCP & A2A Matter for AI Deployment

To scale AI agents across an organization, enterprises need more than smart models—they need standards.

What is MCP?

Model Context Protocol (MCP) is an open interface specification that allows AI agents to interact consistently with enterprise tools, data sources, and other agents—without custom code or proprietary integrations.
While MCP facilitates the access to resources that might be used in multi-agent workflows, the direct communication and coordination between agents is typically handled by Agent-to-Agent (A2A) protocols. MCP uses a JSON-RPC communication to:

• Allow clients (like AI agents) to connect to servers.
• Standardize how requests, responses, and errors are handled between these components.
• Enable modularity—A single tool setup can serve multiple agents, streamlining development.

The goal of MCP is to create a minimal, interpretable interface that lets intelligent agents work across systems without custom APIs or hardcoded integrations.

What is A2A?

Agent-to-Agent (A2A) allows AI agents to delegate tasks, share partial context, and coordinate across functions—using structured, programmatic protocols rather than hardcoded instructions.

Why This Matters

Without common standards, AI agents become fragmented across teams and workflows. MCP and A2A enable composable architecture, traceability, and shared tooling—key to scaling automation without increasing operational risk.

By adopting MCP:

• Tools and resources become composable: Build once, connect many agents.
• Traceable agent decisions: Every interaction is logged and inspectable.
• Cross-functional orchestration made possible: Agent orchestration enables cross-functional coordination and task delegation.

The result is lower engineering overhead during deployment and a consistent architecture. Scaling from isolated use cases to organization-wide AI agents requires shared protocols—not just APIs or refined models. Without standards, enterprise AI becomes hard to audit and expensive to maintain.

Open-source ecosystems, including LangChain, Autogen, and Semantic Kernel, converge on MCP as a shared layer for tool access and context passing. For enterprises, this eases integration and future-proofs internal AI infrastructure.

Why Should Businesses Consider MCP and A2A?

While CEOs don’t need to master the technical details of AI architectures, they do need to assess whether their systems are:

• Modular enough to evolve.
• Transparent enough to audit.
• Scalable enough to grow.

Studies show that more than 80% of AI initiatives underperform or stall—making them significantly riskier than typical IT projects. Success in this domain demands more than automation. It requires agents that can understand, collaborate, and adapt—across platforms, tools, teams, and geographies. This is precisely what Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication enable.

MCP and A2A should be seen as part of the infrastructure that makes scalable AI possible. They’re not solutions in themselves—but they make robust, reusable, and collaborative AI systems possible. Without shared standards, AI rollouts become expensive one-offs. MCP establishes the connections; A2A provides management. Together, they move you to resilient intelligence.
While specific outcomes may vary, AI implementations in IT support have demonstrated up to 40% cost savings and up to 50% time savings.

Inside the Architecture: How MCP & A2A Work

MCP defines a standardized, modular structure where clients request operations and Servers expose tools and data. JSON-RPC ensures standardized, traceable communication—so models, tools, and policies plug in seamlessly.

MCP servers define available tools, data access layers, and interaction templates. Clients act as execution interfaces. The JSON-RPC format ensures every call and response is standardized and traceable. A compatible format across environments ensures enterprises can plug in new models, tools, or policies easily.

A Modular System for Enterprise-Grade AI

Let’s break down the key MCP elements:

• Tools are executable functions—made accessible via the server, invoked by the client. Think of them as APIs that models can call to perform enterprise-level operations—like querying a CRM or triggering a workflow. These aren’t static scripts—they’re dynamic, callable operations the model can reason over.
• Resources are structured data assets—files, database entries, or API payloads. They remain under enterprise control. The model can read them but doesn’t own them. This safeguards integrity and enforces a clean boundary between AI reasoning and enterprise data.
• Prompts serve as organised models. These use variables and predefined instructions to shape model interactions. Prompts convert model behaviour into repeatable, auditable logic. That is when you answer customer enquiries, convert JSON payloads, or summarise legal contracts. Together, these elements form the foundation for AI systems that are modular, auditable, and safe to scale.

MCP Client: A Lightweight Interface for Model Execution

The MCP Client issues calls based on pre-defined prompts and tools—but orchestration logic (like when to call what) sits outside, typically in the agent runtime. It’s worth noting that agents—built on top of MCP, can use Clients to drive intelligent behaviors. For example, a pricing agent could receive a prompt based on real-time supply chain data and invoke a pricing tool to automatically adjust product costs—without human intervention. It’s not guessing. It’s acting within boundaries you’ve set.

Agent-to-Agent (A2A): Real-Time AI Coordination

While MCP standardizes how a single agent operates, Agent-to-Agent (A2A) takes it a step further. It defines how multiple agents communicate. It offers a structured, encrypted, and completely interoperable communication substrate required for independent cooperation.
With A2A:

• Agents can securely share updates about what they’re doing, what they know, and what they need.
• Agents delegate responsibilities dynamically.
• Agents coordinate actions based on shared objectives.

A2A is still an evolving design pattern. While promising, it lacks a unified protocol spec. Today, teams implement A2A through frameworks like AutoGen or custom coordination logic.

Strategic Upside: Why CEOs Should Care

Key outcomes that matter to enterprise leadership:

• Interoperability: With MCP, switching models or vendors doesn’t require rewriting business logic. You get abstraction without lock-in.
• Security & Governance: Fine-grained control over agent access—down to tools, tasks, and data. MCP makes agent behavior predictable and explainable. It also ensures that all actions are fully auditable.
• Compliance: Because MCP standardizes communication formats, it supports detailed logging and traceability—critical for compliance audits and responsible AI governance.
• Adaptability: When priorities change, your architecture doesn’t break. MCP supports plug-and-play upgrades—whether it’s a new language model or a compliance shift.

Assess your existing AI infrastructure based on these criteria:

• Can AI modules integrate without rearchitecting systems?
• Are agent actions traceable and compliant?
• Is collaboration autonomous or human-assisted?
• Can components be swapped without vendor lock-in?

Bottom Line

For CEOs serious about scaling AI—not just experimenting with it—this is the architecture that moves you from pilot to production, from automation to transformation.

MCP Implementation: Best Practices

Integrating the Model Context Protocol (MCP) into your AI infrastructure doesn’t require a complete architectural overhaul. When implemented thoughtfully, MCP enhances how autonomous agents reason, interact, and collaborate across enterprise systems. For CEOs, this means adopting a systems-thinking approach: How do you enable scalable, modular intelligence across functions without compromising control or security?

 Start with a Pilot

Start small. Look for areas where agent-to-agent (A2A) communication can reduce latency or manual intervention. For instance, if your support agents operate without real-time CRM context, MCP can provide the interface to access that data. It will enable better coordination within a broader agent orchestration system.

Choose Open Standards

Avoid proprietary lock-in by selecting an open-standard MCP architecture. Your enterprise should remain flexible—able to integrate new LLMs, APIs, or microservices without rewriting communication protocols.

The MCP server should expose standardized components:

• Tools: Model-invoked operations like database queries or file generation.
• Resources: Application-managed data including APIs, storage, or documents.
• Prompts: Predefined templates for tasks such as summarization or Q&A.

Map Your Context Layers

In AI systems, “context” isn’t just raw data—it includes temporal signals, task relevance, and user intent. MCP enables agents to act not in isolation, but with awareness of their operational environment.

A robust implementation includes a context repository—a shared data layer that maintains evolving state information, enabling agents to coordinate actions with continuity and relevance.

Choose Partners Who Specialize in Agent Orchestration

Work together with engineering teams that have practical MCP framework deployment experience. This will reduce integration risks and accelerate time to value.

For instance, Fingent prioritises security, modularity, and long-term scalability when working with businesses to implement agent-based systems. With tried-and-true design patterns, Fingent customizes design patterns to fit business ecosystems.

Define Success Metrics Early

MCP implementation must translate into measurable business outcomes. Whether you’re targeting a 15% improvement in model accuracy or automating repetitive decision trees, define these metrics early.

When paired with orchestration frameworks, MCP enables real-time visibility into agent workflows—helping your team align AI interactions with measurable KPIs. Engineering efforts should begin only after your success criteria are clearly articulated.

Embrace Incremental Rollout

Deploy MCP incrementally. Begin with isolated, low-risk workflows where output can be quickly validated. Once performance is confirmed, expand to more complex, interdependent functions. This phased approach reduces exposure and allows for faster iteration based on feedback and learning.

Stress-Test A2A Communications

Agent-to-agent communication is the foundation of distributed reasoning. But what happens when an agent disconnects mid-task or misinterprets a shared context?

Design for failure. Run chaos tests that simulate outages, data corruption, and conflicting agent behavior. Your architecture should support retry logic, fallback protocols, and human intervention pathways. Resilience—not just speed—should be the benchmark.

Build in Human Override Mechanisms

As systems scale, autonomous agents must still operate within defined ethical and operational boundaries. Implement policy engines that enforce constraints and human override controls that allow for intervention in edge cases.

These guardrails ensure your AI infrastructure stays compliant, auditable, and aligned with enterprise values.

Treat Your Agents Like Employees

Autonomous agents require structured governance, defined roles, access permissions, audit logs, and performance metrics, similar to how enterprises manage human teams.

Prepare for Disagreement

In modular agent architectures, conflicting outputs are inevitable. One agent may override another; two may interpret context differently. Without conflict resolution protocols, such disagreements can derail workflows.

Implement arbitration logic—whether through rule hierarchies, ensemble models, or escalation to human reviewers. MCP must support not just agent communication, but also reconciliation and collaborative reasoning.

The Challenges

MCP and A2A are powerful—but there are challenges to be aware of so you can deal with them..

Skill Gaps

Most enterprise tech teams are not yet fluent in agent-based coordination. Expect a learning curve in architecture, not just code.

Tooling Immaturity

While libraries like AutoGen and LangGraph are maturing fast, many are still under rapid development. Stability can vary. Documentation often lags.

Standards Fragmentation

Not all “MCP” implementations follow the same conventions. Choose vendors and tools that are interoperable—and be ready to enforce internal standards.

Change Management

Shifting from pipeline automation to agent collaboration requires a mindset change. Some teams may resist. Others may over-engineer. Without constraints, autonomy becomes chaos.

A smart strategy is to treat MCP like an internal protocol—not a one-off project. Invest in internal documentation. Train key leads. And review each rollout with the same rigor as you would a security audit.

Looking Ahead: Future of MCP and A2A Standards

MCP and A2A are still emerging—but the momentum is clear.
Anthropic’s original announcement of MCP provides further context on its origins and intended impact across multi-agent systems.

Open standards are forming. Early implementations are converging around core design principles: JSON-RPC for message passing, shared state objects for coordination, and permissioned tool definitions.

Like Kubernetes standardized containers, MCP is emerging as the control plane for AI agents. Protocols are stabilizing. Tooling is catching up. And early adopters are defining what “good” looks like.

One emerging direction is cross-agent collaboration across platforms—potentially leading to “agent marketplaces,” where enterprises can exchange modular agents that adhere to shared protocols like MCP.

It’s early—but the stakes are high.
Enterprises that adopt MCP now don’t just prepare for the future. They help shape it.

Turning Strategy into Execution—with Fingent

At Fingent, we build custom AI solutions designed to scale and perform—now and in the future. From MCP-compliant architectures to secure A2A pipelines, we turn complexity into clear, measurable results.

At Fingent, we don’t just build—we partner. From architecture to rollout, we make AI reliable, scalable, and aligned with your business goals. Whether you’re launching your first AI agents or managing enterprise-wide intelligent ecosystems, we make sure your AI speaks one language, works seamlessly, and delivers real outcomes.

In the age of autonomous intelligence, being smart isn’t enough. You need smart that works together.
Keep in mind that disjointed AI hinders business progress. Team up with Fingent to power unified, unstoppable intelligence—and lead your industry forward.