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Building AI Agents with Model Context Protocol

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    Ptrck Brgr
    Twitter

The Model Context Protocol (MCP) is Anthropic’s answer to a problem every AI builder knows: integrating models with the right context and tools is slow, brittle, and expensive. Each new application or system integration creates a tangle of bespoke connectors that break easily and don’t scale.

MCP replaces that tangle with a single, open standard. It defines how clients (apps, agents) and servers (systems exposing data or actions) exchange context, capabilities, and prompts. Once your client is MCP-compatible, it can connect to any MCP server with no extra work. That’s the foundation for AI systems that are more modular, composable, and capable.

Main Story

MCP is built on a simple but powerful truth: models are only as good as the context you feed them. Today, context is often bolted on through ad-hoc integrations or manual data injection. MCP standardizes the process with three primitives:

  • Tools — actions the model can invoke
  • Resources — data the application controls
  • Prompts — reusable templates the user controls

This architecture draws inspiration from the Language Server Protocol, which decoupled language tooling from editors. With MCP, any compatible client can speak to any server, whether that server wraps a CRM, a vector database, or an internal API.

“Before MCP we saw a lot of the N×M problem… MCP aims to flatten that and be the layer in between.”

For enterprises, this means clear separation of concerns. One team can own and harden an MCP server for a sensitive database, while multiple application teams consume it without reimplementing access. The result mirrors microservice patterns but for AI context and capability.

Adoption is already broad: over 1,100 community-built servers, plus official integrations from companies like Cloudflare and Stripe. Servers can be minimal—just a few hundred lines of code wrapping an API—or more sophisticated, with logging, transformations, and dynamic resource generation. Because the protocol is simple, even LLMs can generate basic servers automatically.

Beyond integration, MCP is a foundation for building agents. Anthropic’s “augmented LLM” model sees the LLM as the reasoning core, with tools, retrieval, and memory extending its reach. MCP is the open layer that lets these agents discover and use new capabilities even after deployment. Features like sampling (servers can request completions from the client’s LLM) and composability (a process can be both a client and server) enable multi-agent hierarchies and delegation.

The roadmap points to remote servers over SSE with OAuth 2.0, a public MCP registry, and “well-known” discovery endpoints. Combined with Anthropic’s computer-use capabilities, this could let agents prefer APIs when available and fall back to UI automation when not.

Technical Considerations

For engineering leaders, adopting MCP is not just a plug-and-play decision. Key factors to weigh:

  • Integration scope — Start with one or two high-value systems to wrap as MCP servers; expand once you validate the pattern
  • Latency and throughput — The protocol adds minimal overhead, but performance still depends on the underlying system and network
  • Context window limits — MCP makes it easier to pull in rich context, but models still have finite token budgets; design retrieval strategies accordingly
  • Security and privacy — OAuth 2.0 and remote servers extend flexibility but require strong authentication, authorization, and auditing
  • Vendor risk — MCP is open, but server availability and quality vary; decide which capabilities to self-host
  • Skill requirements — Building servers is straightforward, but monitoring, scaling, and securing them requires standard DevOps and API skills
  • Tooling — Check if your orchestration frameworks already support MCP; if not, factor in the work to add a client library

Business Impact & Strategy

The protocol’s most immediate business value is reduced integration cost and faster time-to-value. Instead of building N×M connectors between every app and every data source, you implement each once as a server or a client.

This shift changes KPIs:

  • Time-to-integration drops from weeks to days for new capabilities
  • Maintenance load falls as fixes to a server propagate to all clients
  • Capability expansion accelerates — agents can gain new tools without redeployment

Organizationally, MCP supports a service-ownership model. Teams can publish and maintain their own servers, with clear SLAs, while application teams focus on user experience and workflows. Governance becomes critical: decide which community or public servers are trusted, and set policies for auto-installation.

Risk mitigation should address authentication for remote servers, monitoring for capability drift, and fallback plans if a server becomes unavailable.

Key Insights

  • MCP standardizes how AI clients and servers exchange context, tools, and prompts
  • The open protocol removes the N×M integration problem and enables modular AI architectures
  • Agents gain the ability to discover and use new capabilities post-deployment
  • Features like sampling and composability support multi-agent orchestration
  • Roadmap items like remote servers, OAuth 2.0, and registries will expand discoverability and governance

Why It Matters

For technical teams, MCP means less time on integration plumbing and more on product differentiation. For business leaders, it means faster deployment, lower costs, and AI systems that can grow in capability without constant rebuilds.

This is not just a developer convenience — it is an architectural shift. By making AI context and capabilities as discoverable and composable as APIs, MCP lays the groundwork for scalable, adaptive agents that fit enterprise realities.

Conclusion

MCP turns AI integration from a bespoke, brittle process into a standardized, composable layer. It gives teams a shared language for connecting models to the tools and data they need, while keeping ownership and security clear. For leaders building AI-powered products or internal tools, now is the time to test MCP on a small scale and prepare for its broader adoption.

Watch the full workshop here: https://www.youtube.com/watch?v=kQmXtrmQ5Zg