Leading with AI: An Intro to MCP

Is your AI deployment stuck in a sandbox—great at theoretical demos but disconnected from the real-world systems that matter? Continuing the conversation from my earlier Goose article on AI orchestration, we now turn to Anthropic’s Model Context Protocol (MCP). This emerging open standard aims to unify AI integrations so your models can securely access enterprise data, execute tasks, and deliver tangible business outcomes. Below, we’ll explore how MCP addresses critical enterprise concerns—data privacy, compliance, and governance—while offering a scalable path for AI-driven innovation.

1. Introduction

Enterprises worldwide face a daunting challenge: integrating AI models into complex environments without sacrificing data privacy or security. Legacy mainframes, multiple cloud repositories, and strict compliance standards make “last mile” AI integration risky and labor-intensive.

Anthropic’s Model Context Protocol (MCP) was created to standardize how AI agents connect to diverse data sources and tools, drastically reducing integration overhead. Building on insights from the Goose article—where an open-source agent orchestrated code tasks—this piece highlights how MCP can unlock broader enterprise potential. We’ll also show how this protocol can unify your AI landscape, from finance to healthcare to DevOps.

2. Technical Overview of MCP

2.1 Purpose & Rationale

Think of MCP as the “ODBC for AI” or “GraphQL for LLMs.” Instead of each AI model juggling custom APIs or ad-hoc WebSocket calls, they communicate via a single open standard. That means fewer one-off connectors, faster deployment cycles, and better governance.

2.2 Client–Server Architecture

• MCP Client (AI Model/Application): Any LLM (like Anthropic Claude or an open-source agent such as Goose) that requests data or performs tasks.
• MCP Server (Adapter/Connector): Exposes certain tools/data via JSON-RPC 2.0. This can be on-premises, cloud-hosted, or containerized.

Because it’s transport-agnostic (supporting HTTP, WebSockets, etc.), MCP easily fits into existing infrastructures.

2.3 JSON-RPC Messaging

MCP relies on JSON-RPC, allowing standardized requests (e.g., “getResource,” “executeTool”) and structured responses. A hypothetical snippet might look like this:

{
  "jsonrpc": "2.0",
  "method": "executeTool",
  "params": {
    "toolName": "DatabaseQuery",
    "query": "SELECT * FROM transactions WHERE amount > 10000"
  },
  "id": 1
}
        

Such a format ensures the AI can discover tools, pass parameters, and retrieve results consistently—no custom protocol needed each time.

2.4 Key Primitives

Anthropic’s MCP specification outlines five core primitives that manage context and tool invocation:

1. Prompts – Predefined instructions or templates shaping AI responses.
2. Resources – Data objects (reports, docs, transaction logs) the server shares for context.
3. Tools – Functions the AI can call (“SendEmail,” “RunSQL,” “PostToSlack,” etc.).
4. Roots – References to local or remote file systems the AI might expose for deeper context.
5. Sampling – A mechanism for the server to request AI-generated text during workflows (useful for multi-step tasks).

2.5 Security & Governance

Enterprises can self-host MCP servers, controlling exactly what data or tools the AI can access. With robust logging and audit trails, you’ll know exactly when the AI invoked a particular action—helping maintain compliance and privacy. Fine-grained permissioning ensures a regulated bank or hospital can restrict the AI’s visibility to only the appropriate subsets of data.

3. Comparative Analysis

3.1 MCP vs. LangChain’s Memory Management

LangChain focuses on internal memory (storing conversation logs, chaining prompts), yet each external data/tool integration is typically coded anew. MCP standardizes these integrations so multiple AI apps can share connectors, avoiding repeated effort. LangChain remains ideal for rapid prototyping, but MCP offers a more scalable approach for multi-app ecosystems.

3.2 MCP vs. Rasa’s Dialogue Policies

Rasa employs policy-driven flows—ideal for deterministic chatbots. MCP is more open-ended, letting the AI call tools or fetch resources on the fly. Rasa’s strict policy approach often suits compliance-driven dialogues; MCP-based LLMs require guardrails to prevent undesired tool use but excel in flexible, evolving scenarios.

3.3 MCP vs. Custom WebSocket Integrations

Many organizations used bespoke WebSocket or REST APIs for AI, which can become cumbersome once multiple data sources or AI clients proliferate. MCP’s consistent JSON-RPC messages let you “write once, reuse everywhere,” making it simpler to scale cross-system integrations.

4. Practical Enterprise Use Cases

4.1 Finance – Compliance & Risk Analysis

A multinational bank might:

1. Call a “FraudScan Tool” on recent high-value transactions (Tools).
2. Retrieve relevant AML laws or compliance guidelines (Resources).
3. Generate a summary referencing specific policy sections—helping compliance officers triage suspicious cases.

All data remains behind the bank’s firewall, ensuring strict confidentiality.

4.2 Healthcare – Clinical Support & Privacy

In a hospital context:

1. Fetch EHR summaries (de-identified Resources) with sensitive fields redacted.
2. Check a “ClinicalGuidelines Tool” for the latest treatment protocols.
3. Draft a recommended plan or documentation for physician review.

Because the EHR connector enforces what data the AI can see, patient privacy is preserved.

4.3 SaaS – DevOps & Workflow Automation

SaaS teams juggle Git repositories, CI/CD pipelines, and multiple communication channels:

1. Commit code via a GitHub MCP server.
2. Trigger tests, then retrieve build results as Resources.
3. Notify DevOps if tests fail, summarizing logs on Slack.

This end-to-end orchestration reduces context switching, accelerating release cycles.

5. Instructions and Learning Resources

5.1 Actionable Steps for Technology Leaders

1. Choose an MCP-Compliant AI Client: For instance, Anthropic Claude or open-source Goose.
2. Stand Up MCP Servers: Focus on high-impact connections—like CRMs, EHRs, or code repos.
3. Plan Permission Models: Assign role-based access to tools/data, and keep logs for auditing.
4. Pilot in a Sandbox: Start with a test environment or non-critical data.
5. Iterate & Expand: As you see results, add more servers (ERP, finance ledgers, etc.).

5.2 Recommended Resources

• Anthropic’s official documentation on Model Context Protocol.
• Open-source SDKs available in Python, TypeScript, and Java.
• Goose examples illustrating how an AI agent can leverage MCP.
• Docker Blog posts on containerizing MCP servers.
• Community forums where practitioners share experiences and best practices.

5.3 Dos and Don’ts

• Do thoroughly plan your permission model before exposing sensitive data.
• Don’t give an AI free rein over production systems without gating or monitoring.
• Do maintain and regularly audit logs.
• Don’t skip user acceptance tests—verify that AI actions align with business logic.

6. Leveraging Prior Research and the Goose Article

This piece is a follow-up to my article on Goose, which showcased how an open-source agent orchestrates tasks like code scaffolding and test automation. MCP extends that concept to a universal layer for multi-system integration. Whether retrieving knowledge base content or scanning financial ledgers, the AI can act consistently across numerous tools once MCP is in place.

7. Conclusion & Next Steps

By adopting MCP, organizations shift away from piecemeal integrations toward a standardized protocol that unifies AI, data, and tools:

• Simplified Integration: Less repetitive coding for each data source.
• Enhanced Governance: Centralized logs and granular permissions keep data secure.
• Interoperability: The MCP ecosystem is evolving, enabling new servers and AI clients to plug in easily.
• Faster ROI: Reduced engineering overhead, faster deployments, and more agile experimentation.

Ready to begin? Explore the available documentation and open-source projects, stand up a low-risk proof-of-concept, and discover how MCP can propel your AI initiatives beyond siloed POCs into a truly integrated enterprise solution.

(Stay tuned for more AI tips and tricks!)