The Model Context Protocol (MCP) is an open standard that enables AI applications to securely connect with external tools, data sources, and systems through a unified interface. It provides a standardized way for AI agents and large language models to interact with the real world — querying databases, calling APIs, triggering workflows, and accessing enterprise resources.
What is MCP?
MCP acts as a universal adapter between AI models and external capabilities. Without MCP, every AI integration requires custom code for each tool or data source. MCP standardizes these connections so that any MCP-compatible AI application can use any MCP-compatible tool, much like how USB standardized hardware connections.
The protocol defines three types of capabilities that servers can expose:
- Tools — Functions the AI can call to perform actions (run a query, create a map layer, send an email)
- Resources — Data sources the AI can read (databases, documents, APIs)
- Prompts — Reusable templates for common interactions
How MCP Works
MCP follows a client-server architecture:
- MCP Server — A service that exposes tools, resources, or prompts following the MCP specification. For example, a spatial analytics MCP server might offer tools for geocoding, isoline generation, and spatial joins.
- MCP Client — An AI application (like Claude, ChatGPT, or a custom agent) that connects to MCP servers to discover and use available capabilities.
- Discovery — The client queries the server to learn what tools are available, their parameters, and their descriptions.
- Invocation — When the AI decides a tool is needed, it sends a structured request to the server and receives a structured response.
MCP in Spatial Analytics
MCP is particularly relevant for spatial analytics because it allows AI agents to perform complex geospatial operations without the AI needing built-in spatial capabilities. A spatial MCP server can expose tools like:
- Running spatial SQL queries against a data warehouse
- Generating isolines (drive-time or walk-time areas)
- Performing geocoding and reverse geocoding
- Creating and styling map visualizations
- Executing spatial statistics and enrichment workflows
CARTO enables users to deploy their spatial analysis workflows as MCP tools, which can then be accessed from AI platforms like Claude or Google Agentspace. This means organizations can leverage their existing CARTO workflows from within any MCP-compatible AI system.
Why MCP Matters for Enterprise AI
- Interoperability — AI tools from different vendors can work with the same MCP servers, avoiding vendor lock-in
- Security — MCP servers can enforce authentication, authorization, and audit logging at the tool level
- Governance — Organizations control which tools are exposed and what data they can access
- Composability — AI agents can combine tools from multiple MCP servers to accomplish complex tasks
Frequently Asked Questions
Who created MCP?
MCP was created by Anthropic and released as an open standard. It is supported by a growing ecosystem of AI platforms and tool providers.
How is MCP different from an API?
APIs are custom integrations between two specific systems. MCP is a standardized protocol that allows any compatible AI client to discover and use any compatible tool server. Think of APIs as point-to-point connections and MCP as a universal plug system.
