MCP Resources
MCP resources are addressable read-only context — files, records, docs — that a client can pull into the model instead of calling a tool.
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Resources are identified by URI and fetched on demand, which keeps large context out of the prompt until it is needed. Use resources for material the model reads (documentation, a spec, a record) and tools for actions it performs. Mixing the two into one giant tool is the most common MCP design mistake.
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What is MCP Resources?
Resources are identified by URI and fetched on demand, which keeps large context out of the prompt until it is needed. Use resources for material the model reads (documentation, a spec, a record) and tools for actions it performs. Mixing the two into one giant tool is the most common MCP design mistake.
What is an example of MCP Resources?
A knowledge-base server exposes `kb://policies/refunds` as a resource; the agent pulls it only when a refund question arrives.
Why does MCP Resources matter?
MCP resources are addressable read-only context — files, records, docs — that a client can pull into the model instead of calling a tool.
What should I learn after MCP Resources?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
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Related concepts
The vocabulary this page depends on.
- →MCP (Model Context Protocol)
Open standard letting AI clients call external tools, data and prompts.
- →Context Engineering
Context engineering is the discipline of deciding exactly what information enters a model's context window, in what order and at what cost.
- →MCP Server
An MCP server exposes tools, resources and prompts from one system so any MCP-compatible AI client can use them over a standard protocol.
- →MCP Client
An MCP client is the AI-side host that discovers servers, lists their tools and routes the model's calls to them.
Related workflows
Turn this into a repeatable process.
- →Build an MCP Server for Your Own App
Expose your product's capabilities to every AI client with one server.
- →How to Create a Website with AI
Go from idea to a live, custom-domain website in one afternoon using AI builders.
- →How to Build an AI Content System
A repeatable pipeline that turns one input into publish-ready content across every channel.
- →How to Start a Niche Website with AI
Pick a niche, validate demand, build the site, and publish ranking content using AI end-to-end.
Related tool stacks
The tools that run it in production.
- →MCP Integration Stack
Build, deploy and secure MCP servers that real AI clients can use.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
Comparisons & alternatives
Pick between the options.
- →RAG vs Long Context Windows
Is RAG obsolete now that context windows are 1M+ tokens? Not quite — here's when each wins.
- →ChatGPT vs Claude
Two leading conversational AI assistants compared across reasoning, writing, coding, and pricing.
- →Lovable vs Bolt
Two AI app builders compared on speed, backend, deployment, and production readiness.
- →OpenAI API vs Anthropic API
Choosing between the two leading LLM API providers for production apps.