How the Model Context Protocol Works: A Technical Deep Dive
The Model Context Protocol (MCP) is emerging as one of the most critical new standards in generative AI — a way for large language models (LLMs) to interact with enterprise systems safely, dynamically, and contextually.
For technical leaders, AI engineers, and architects, understanding how MCP works is essential for unlocking secure, contextual AI integrations across search, commerce, and operations. It’s not just another framework — it’s the connective tissue that makes AI useful within your organization’s existing stack.
Lucidworks, a leader in enterprise search and product discovery, is uniquely positioned to help organizations implement MCP-driven architectures that combine intelligence, context, and governance.
This post takes a technical deep dive into MCP — its structure, message flows, and how it connects with related protocols like ACP (Agentic Commerce Protocol) to support AI-driven transactions.
What is MCP and Why It Matters
Model Context Protocol (MCP) is an open standard that defines how AI models — especially LLMs — communicate with external tools, APIs, and data sources in a structured, discoverable, and secure way.
At its simplest, MCP standardizes how models find and use external context. Think of it as a JSON-RPC interface for AI: the model acts as the client, and the enterprise’s tools and data systems act as MCP servers.
In practice:
- The model (like ChatGPT or another LLM) queries what tools or data are available via MCP.
- The MCP server exposes capabilities — APIs, data queries, document stores, and even prompt templates.
- The model invokes those capabilities dynamically, with schema validation, authentication, and logging.
This standardization is key because, until MCP, every AI integration was custom, requiring brittle, one-off connectors between each model and each enterprise system.
With MCP, we now have a shared language for AI-to-system communication, similar to how REST or GraphQL standardized web APIs.
MCP Architecture: A Client-Server Design
Architecturally, MCP is modeled as a client-server protocol. The LLM or agent acts as the client, while one or more MCP “servers” expose structured capabilities.
| Component | Role | Description |
|---|---|---|
| MCP Client | Initiates communication | Usually, the LLM or agent that wants to use enterprise tools. |
| MCP Server | Exposes capabilities | The system offers APIs, data sources, or functions that the model can call. |
| Schema Registry | Defines structure | This document contains schemas for message types, tool definitions, and data formats. |
| Transport Layer | Handles communication | Typically JSON-RPC over WebSocket or HTTP. |
A typical MCP exchange looks like this:
- The model sends a discovery request: “What tools are available?”
- The MCP server returns a list of tools or data sources, with schemas and metadata.
- The model chooses a tool and sends an invocation request, providing input parameters.
- The server executes the request (e.g., a search query or data fetch) and returns structured results.
- The model uses those results to refine its next action or generate a response.
This architecture allows models to work with live enterprise data — without direct database access or unsafe plugin architectures.
Message Flow: How MCP Communicates
MCP’s message system is inspired by JSON-RPC, ensuring interoperability and simplicity. Each message is a JSON object with a defined structure.
Example: Tool Discovery
{
“jsonrpc”: “2.0”,
“method”: “mcp.listTools”,
“id”: 1
}
Response:
{
“jsonrpc”: “2.0”,
“result”: [
{
“name”: “getCustomerOrder”,
“paramsSchema”: {
“type”: “object”,
“properties”: { “customerId”: { “type”: “string” } }
},
“returns”: “OrderDetails”
}
],
“id”: 1
}
Example: Tool Invocation
{
“jsonrpc”: “2.0”,
“method”: “mcp.invoke”,
“params”: {
“tool”: “getCustomerOrder”,
“args”: { “customerId”: “12345” }
},
“id”: 2
}
Response:
{
“jsonrpc”: “2.0”,
“result”: {
“orderId”: “A-99821”,
“status”: “Delivered”,
“total”: 129.99
},
“id”: 2
}
These structured exchanges are what make MCP both machine-readable and governable. IT teams can log, monitor, and restrict access at a granular level.
MCP Schema and Validation
Every message and tool definition in MCP follows a strict JSON schema. This enforces data types, structures, and rules for input/output validation.
| Schema Element | Purpose |
|---|---|
| paramsSchema | Defines input parameters for each tool |
| resultSchema | Defines expected output types |
| metadata | Describes tool purpose, ownership, or compliance tags |
| auth | Defines authentication and authorization scope |
This schema-based approach prevents errors, enforces access controls, and allows dynamic discovery — the model can safely explore new tools without risk of misuse.
Integrating MCP in Enterprise Systems
Enterprises can add MCP compatibility by wrapping existing APIs or services with a lightweight MCP server layer.
Step-by-step example:
- Identify an API or data source, such as product inventory or CRM.
- Define an MCP schema for each callable function.
- Deploy an MCP server module that exposes those capabilities.
- Register it in your AI agent’s configuration.
- The LLM can now dynamically query or invoke those tools through MCP.
For instance, a Lucidworks-powered search API could expose its query and recommendation functions through MCP:
{
“name”: “searchProducts”,
“paramsSchema”: {
“type”: “object”,
“properties”: {
“query”: { “type”: “string” },
“filters”: { “type”: “object” }
}
},
“returns”: “SearchResults”
}
This enables AI agents to use Lucidworks’ search intelligence directly, combining LLM reasoning with Lucidworks’ precision relevance and merchandising data.
MCP and ACP: From Search to Transaction
While MCP connects models to data and tools, the Agentic Commerce Protocol (ACP) extends this to transactions — allowing AI agents to complete purchases securely.
- MCP: Handles context and capability discovery (e.g., find product data, query systems).
- ACP: Handles commerce and payments (e.g., checkout, fulfillment, returns).
For example:
- The agent uses MCP to call searchProducts(“wireless keyboard”).
- It evaluates results and user preferences.
- It invokes ACP to complete the checkout securely.
Together, MCP and ACP form the foundation of agentic ecosystems — where AI systems can research, recommend, and transact safely under enterprise governance.
Security and Governance in MCP
Security is built into MCP at multiple layers:
| Layer | Security Feature |
|---|---|
| Authentication | Uses OAuth/JWT tokens for model-to-server access |
| Authorization | Role- and scope-based permissions on tools |
| Data Governance | Fine-grained audit trails for all tool invocations |
| Policy Controls | Optional rule sets for compliance (GDPR, PII, etc.) |
Because models dynamically invoke tools, observability is crucial. Enterprises can log every invocation, parameter, and response — providing full traceability for compliance and debugging.
Lucidworks’ platform already offers observability, policy enforcement, and data masking that complement MCP-based architectures — making it a trusted integration point between models and enterprise systems.
Comparing MCP to Context Windows
The context window of an LLM limits how much information it can “remember” at once. MCP provides a different kind of scalability — instead of making the model bigger, it makes it smarter by letting it query external context on demand.
| Approach | Method | Limitation |
|---|---|---|
| Bigger Context Windows | Add more tokens to the model memory | Expensive, brittle, memory-heavy |
| MCP Protocol | Fetch live data when needed | Requires structured integration |
For AI-driven search and discovery systems, MCP is far more efficient — giving the model just-in-time access to Lucidworks’ indexed data, recommendations, and personalization features.
Future Outlook: MCP as the New AI Infrastructure Layer
Just as APIs revolutionized software integration, MCP may become the universal layer for AI-to-system communication.
We can expect:
- Tool marketplaces for MCP servers (e.g., CRM, ERP, PIM connectors).
- Enterprise AI orchestration platforms built around MCP standards.
- Auditable AI pipelines, where every tool invocation is logged and governed.
Lucidworks’ approach — blending AI-driven discovery with enterprise-grade governance — aligns directly with this trend. It bridges the gap between open-ended AI reasoning and structured enterprise reliability.
Key Takeaways
- MCP (Model Context Protocol) provides a standardized, secure interface for connecting LLMs with enterprise tools, data, and APIs.
- It uses JSON-RPC messaging and schema validation to ensure safe, dynamic capability discovery.
- ACP (Agentic Commerce Protocol) complements MCP by managing transactions and payments.
- Lucidworks’ platform integrates seamlessly with MCP-based architectures, offering governance, observability, and relevance at enterprise scale.
- MCP is not just a protocol — it’s the next evolution of enterprise AI infrastructure.