MCP Resources Overview

Resources in MCP (Model Context Protocol) servers are structured data objects that provide contextual information to AI agents and LLM clients. Think of them as a standardized way to expose and access various types of content that can inform and enhance AI interactions.

The Core Concept

Resources are essentially data containers that hold information in various formats. It can be something like a simple text document or structured data files like CSV, JSON, or even images. They serve as a bridge between static information and dynamic AI interactions, allowing agents to access relevant context when needed. Unlike tools (which perform actions) or prompts (which guide interactions), resources are passive information sources that can be queried, retrieved, and used to inform the LLM or AI Agent about necessary context.

How Resources Work in Practice

Resources are designed to integrate seamlessly with context packs, allowing them to be bundled with related tools and prompts for specific workflows. This way resources become part of curated collections of additional information that can be used alongside tools, guided by prompts. For example:

• A marketing context pack might include brand guidelines (text resources), logo files (image resources), and campaign data (CSV resources).
• A development context pack could contain project documentation (markdown resources), configuration files (JSON resources), and coding standards (text resources).

Real-World Examples

Consider a documentation resource with the URI company://docs/api-reference containing complete API documentation in markdown format. This becomes invaluable when an AI agent needs to help developers integrate with internal services. Similarly, an analytics resource at analytics://reports/monthly-metrics might contain CSV data with monthly performance metrics and KPIs, enabling the agent to provide data-driven insights during planning discussions. Configuration resources like config://deployment/staging hold JSON parameters for staging environments, allowing agents to understand system contexts when troubleshooting or deploying applications.

Benefits of the Resource System

Resources assist AI Agents and humans using LLM clients to access and utilize information. Rather than hunting through disparate systems or relying on potentially outdated copies, they can access a single source of truth that reduces inconsistencies and ensures they're working with current data. When resources are organized properly into context packs, agents or LLM clients can automatically have access to exactly the right information for their specific tasks without being overwhelmed by irrelevant data.

Resources in MCP servers give AI agents structured access to information. Instead of being limited to their training data, AI can now pull from your actual documents, databases, and files when they need context for a task.

This matters because it means AI can work with your real information rather than making assumptions or giving generic answers. Whether you're building automated systems or just chatting with Claude about your project, the AI can reference the specific documentation, data, and configurations that actually matter to your work.

The result is AI interactions that are relevant to your actual situation instead of theoretical examples.