AI Agent Composition
What Is an MCP-Powered Agent?
In MindCore, an AI Agent is a composable automation unit that uses an AI model (like GPT-4, Claude, or LLaMA) to reason, combined with one or more MCP Modules to act on real-world data. Agents are context-driven — meaning they make decisions based on live inputs and external logic, not just prompts.
Agents can monitor markets, scrape data, trigger alerts, send messages, and much more — all powered by modular logic blocks.
Core Structure of an Agent
Every agent is composed of three essential layers:
1. LLM Core (Reasoning Engine)
The AI model that interprets data, applies logic, or generates responses.
Supported models: GPT-4, Claude, LLaMA, and more.
Optional — agents can also run fully rule-based with no LLM.
Use Cases:
Summarize market sentiment
Interpret analytics data
Make contextual decisions (e.g., should I alert or wait?)
2. MCP Modules (Action Layer)
Reusable logic units that fetch data or execute tasks.
Agents can use any combination of:
Data Feed MCPs (e.g., token prices, volumes)
Analytics MCPs (e.g., Twitter mentions, GA data)
Web Scraping MCPs (e.g., NFT floor prices, news headlines)
These modules provide the raw context for the agent to process.
3. Logic & Triggers (Behavior Layer)
This defines how and when the agent operates.
You can configure:
Trigger Types:
On-demand (manual)
Scheduled (every X min/hour/day)
Event-based (if module output matches condition)
Decision Rules:
Use LLM logic, condition blocks, or both
Chain multiple MCP outputs into a unified rule set
Actions:
Send alerts (Telegram, Discord, email)
Call webhooks
Store output onchain or in external DBs
Example Agent Composition
Agent: ETH Breakout Alert
LLM Core: Claude
Modules:
ETH Price Feed MCPTwitter Volume MCP
Logic:
If
price > $3000ANDtweet volume +50%→ alert
Trigger: Every 5 minutes
Action: Send message to Discord
Building Agents Visually
Using MindCore’s No-Code Builder:
Drag and drop MCP Modules onto the canvas
Add LLM model
Define logic via condition blocks or prompts
Set triggers and actions
Click Deploy
No backend, no DevOps — just ideas into execution.
Best Practices
Keep agents focused (1 purpose per agent)
Use version control when updating logic
Start simple, then layer complexity
Test agents with real data before deploying on schedule
Combine open/public MCPs with your private logic for flexibility
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