Model Context Protocol (MCP)

MCP defines a strict client-server relationship. The MCP Host (the AI application) creates MCP Clients that maintain 1:1 connections to MCP Servers. This architecture ensures that the AI model's reasoning is cleanly separated from the execution of tools.

• Host: The orchestrator (e.g., Claude Desktop, an IDE).
• Client: A stateful session managing the connection to a specific server.
• Server: A lightweight program exposing specific capabilities (filesystem access, database queries, API calls).

This separation allows a single AI agent to connect to multiple specialized servers simultaneously without coupling the model to any specific implementation.

Instead of hard-coding API schemas into prompts, MCP enables dynamic discovery. Upon connection, the client issues a tools/list request, and the server responds with a JSON Schema manifest of its available functions.

• Self-Describing: Each tool includes a name, description, and structured inputSchema.
• Model-Chosen: The AI model autonomously decides when and how to call a tool based on the user's query.
• Late Binding: New tools can be added to a server without updating the model or the host application.

This mechanism transforms the model from a static chatbot into an agent capable of exploring and utilizing any connected digital resource.

Beyond executing actions, MCP standardizes read access to data via Resources. Resources are identified by URI schemes and provide structured, read-only context injected into the model's prompt.

• Resource URIs: Use schemes like file://, postgres://, or custom manufacturing:// to identify data sources.
• Content Types: Supports both text (text/plain) and binary (image/png, application/pdf) content for multimodal models.
• Templates: Servers can expose parameterized URI templates (e.g., weather://{city}/forecast) for dynamic data retrieval.

This allows an agent to securely pull live production schematics, database records, or documentation directly into its reasoning context without manual copy-pasting.

MCP includes explicit primitives for human oversight of sensitive operations. Servers can request human approval before executing high-stakes actions, preventing autonomous agents from making irreversible mistakes.

• sampling/createMessage: Allows the server to request the LLM to generate text, but the host can route this to a human for review.
• elicitation requests: The server can explicitly ask the human user for a decision or additional information mid-workflow.
• Audit Trail: Every tool call and resource access is logged, providing a complete chain of custody for compliance.

In a manufacturing context, this ensures an agent cannot autonomously alter a physical production line setpoint without explicit operator confirmation.

Security is a first-class design principle. MCP supports multiple transport mechanisms, each designed for different deployment topologies, all with mandatory encryption and authentication.

• stdio: For local inter-process communication. The client spawns the server as a subprocess, ensuring data never leaves the machine.
• Streamable HTTP: For remote client-server connections over the network, using OAuth 2.0 for authorization.
• Session Isolation: Each client connection is an isolated session, preventing cross-contamination of sensitive enterprise data.

This architecture allows a factory-floor agent to securely connect to a local OPC-UA server via stdio while simultaneously querying a cloud-based ERP system via authenticated HTTP.

MCP servers can expose reusable Prompt Templates that standardize how models interact with specific tools. This ensures consistent, high-quality outputs across different models and use cases.

• Pre-crafted Prompts: Servers provide domain-specific prompts (e.g., analyze-defect) that include the correct context and instructions.
• Argument Binding: Templates accept dynamic arguments, allowing a generic prompt to be specialized for a specific part number or work order.
• Discoverable: Clients can list available prompts via prompts/list, allowing users to select from a menu of expert-crafted interactions.

This feature acts as a knowledge capture mechanism, allowing senior engineers to encode their diagnostic workflows into reusable, agent-accessible templates.

The algorithmic process of analyzing and ordering manufacturing tasks based on prerequisite constraints. MCP exposes tool dependencies as a graph, allowing agents to resolve execution order before invoking downstream services.

• Prevents work-in-process starvation and assembly line stoppages
• Uses topological sorting to linearize partial orders
• Critical for orchestrating multi-step MES workflows

A distributed transaction pattern where a long-running business process is split into a sequence of local transactions, each with a defined compensating action. MCP tools can implement sagas to maintain consistency across microservices.

• If a step fails, compensating transactions roll back prior steps
• Ensures eventual consistency without distributed locks
• Essential for orchestrating ERP-to-MES production order flows

A workflow design pattern where an autonomous agent pauses execution and escalates a critical exception or low-confidence decision to a human operator. MCP can route these escalations through structured notification channels.

• Agent presents context, proposed action, and confidence score
• Operator approves, rejects, or modifies before execution resumes
• Critical for safety-rated manufacturing operations