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Model Context Protocol (MCP) with ColdFusion
Last update:
May 18, 2026
Use the Model Context Protocol (MCP) to connect ColdFusion applications to AI agents and external systems through a standardized interface.
What is MCP
The Model Context Protocol (MCP) is an open protocol that defines how AI agents (clients) interact with external systems (servers) using a standardized, auditable interface.
As AI systems evolve from simple chatbots to agents that perform complex tasks, they need a reliable way to access tools and data. MCP defines how AI agents talk to external systems in a consistent, auditable way.
MCP standardizes access to:
- Tools: Actions the model can invoke (for example, search, calculate, or update records)
- Prompts: Reusable prompt templates that describe workflows
- Resources: Read-only contextual data (files, logs, domain-specific data)
- Optional flows: Sampling, elicitation, logging, and roots
When to use MCP in ColdFusion
Use MCP when your ColdFusion application needs to:
- Call AI-enabled tools — Invoke tools exposed by external or internal systems (for example, Jira, Wiki, or custom APIs)
- Expose ColdFusion business logic — Make your CFCs, services, and data available as AI-callable tools
- Share prompts and resources — Centralize prompt templates and data so multiple AI clients can reuse them
- Build agentic workflows — Create AI-assisted flows without writing custom glue code for each integration
| Scenario | MCP role | ColdFusion approach |
|---|---|---|
| Chatbot that fetches Jira tickets | Client | Use MCPClient to connect to Jira MCP server |
| Expose patient records to an AI assistant | Server | Use MCPServer to wrap healthcare CFCs |
| Share a discharge-summary prompt across apps | Server | Register prompts on your MCP server |
| Multi-tenant app with per-tenant tools | Both | Client for external MCPs; server for your tools |
MCP is optional. You can call tools directly from CFML, but MCP becomes valuable when you need:
- Standardization: One protocol for all AI integrations instead of custom APIs per vendor
- Governance: Auditable tool calls, centralized prompt management, and controlled resource access
- Reuse: Share tools, prompts, and resources across multiple AI clients and applications
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