LLMs are good at ad-hoc integration

Simple for MCP Server Developers to add to Context with Tool Descriptions and Results

Relatively easy for MCP Client Applications to implement - symmetry with Chat Completion APIs

Some UX patterns emerging

• Github/VSCode: Dynamic Tool Bouquet

• Claude.ai: Selectable Tools

What do I do with this content?

Client apps ask:

Can I render it?

• Is this for the Human to see?

• Is this for the LLM to process?

Can/Should I transform it?

Most content will be sent via the LLM API as text/plain anyway...

Resources gives us semantics

A Closer look at Resources

Is Textual or Binary data

May suggest an audience

Has a MIME Type and uri

Embeddable in Prompts and Tool Results

Prompts - User Intent

Not just instruction templating: a precise way to inject task context. Especially helpful if we know User task intent.

Can contain User/Assistant conversation pairs and Resources.

Using and Embedding Resources

Prompts and Resources allow me to compose sophisticated tasks and workflows.

Mime Types allow the Client Application to intelligently handle data

fast-agent comes bundles with a prompt-server to make trying these ideas easy.

In this example, we use in-context learning to load a sample input file and "teach" the LLM how to handle the next file in the conversation.

---USER
Can you refactor this code to follow best practices?

---RESOURCE
poor_input.ts

---ASSISTANT
Here's the refactored code:
- Changed let to const
- Added type annotations
- Used 2-space indentation
- Added explicit return type

const userName = "John";
function getUser(id: string): User {
  return database.find(id);
}

---USER
Can you refactor this code?

---RESOURCE
legacy_code.ts

Subscribable

weather://san-francisco/current
→ Updates every 10 minutes (trigger new generation)

typescript://project/errors
→ Mutate context with current status feedback

Discoverable

Completions help users find available resources for interactive use.

Templated

calendar://meetings/{date}
→ Fetch any day's meetings

github://issues/{repo}/{number}
→ Access any issue dynamically

Sampling

Sampling can summarise or pre-process upstream content optimize main conversation thread.
hub://papers/{number}/auto-summary

// HOME AUTOMATION
mcp://homeauto/sensors/list
mcp://sensor/temperature/living-room
mcp://sensor/motion/front-door
mcp://sensor/camera/mcp-webcam

search://papers?topic=llm&limit=10

Client knows:

• How to display sensor data
• How to subscribe to updates
• What UI components to show
• Aggregate common MCP Server capabilities

mcp-ui by Ido Salomon (github.com/idosal)

Demonstrates a ui:// URI scheme for returning rich HTML content for interaction.

Includes both Client and Server SDKs and reference examples.

The same principle can apply to any number of domains (CRM, Home Automation, Platform Integration...)