Connect Your Agents to the MCP Server

Recap So Far

• Day 1: Introduced the Model Context Protocol (MCP) and why it matters

• Day 2: Built the MCP Server that handles context for inference orchestration

Until now, the frontend drove everything — it requested context from MCP and passed it to the agents.

But what if your agents could manage themselves? What if they could fetch memory, update state, adapt roles, and operate without orchestration glue?

That’s what today is about.

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From Orchestrated to Autonomous

In many LLM-based workflows, the frontend or coordinator acts as the brain. That works — but only to a point.

As your systems grow more complex:

• Agents need autonomy

• They may trigger sub-agents

• They should be able to operate independently of a user interface

This means agents must talk to MCP directly.

Today, we expand our architecture so agents can request context bundles themselves.

This design unlocks agent autonomy, where the model can reason over its own past, current task state, memory, and tools — all without frontend involvement.

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Why Agents Should Pull Their Own Context

When agents rely on frontends for context, you introduce brittle dependencies:

• Agents can’t be reused easily across environments

• Debugging context issues requires full-stack tracing

• Real-time reactions are delayed by orchestration lag

By allowing agents to pull their own state, you:

• Enable background or CRON-like execution

• Allow persistent context recall

• Build modular agent services that evolve with their purpose

Think of agents as microservices. Context is their configuration file.

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Architecture: Agent-Initiated Context Flow

New flow:

Frontend-Orchestrated:
User → Frontend → MCP → Agent

Agent-Orchestrated:
Agent → MCP → Inference

       +-------------+
       |  Agent GPT  |
       +-------------+
              |
              v
       +-------------+        +------------------+
       |  MCP Server | -----> | Memory + Goals   |
       +-------------+        +------------------+
              |
              v
       [Context Bundle]

Breakdown:

• Agent GPT makes a structured HTTP request to MCP

• MCP fetches all relevant details for the agent — persona, memory, system prompt, etc.

• Agent receives the context bundle, reasons, and acts

This removes any dependency on the frontend. Agents are now runtime-aware actors — capable of pulling their state and recontextualizing themselves.

This pattern forms the basis for:

• Agent polling loops

• Asynchronous task workers

• Scheduled jobs

• Chainable agents

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Use Case: Autonomous Task Loop

Imagine you have a ResearchAgent (let's call it TrendWatcherGPT) that loops every hour.
It needs to:

1. Wake up

2. Request context from MCP

3. Use tools/memory to take next step

4. Save progress (to MCP or external store)

5. Sleep again

This is useful for agents like:

• Feed Watchers (e.g., price monitoring)

• Project Agents (managing async updates)

• Background Taskers (handling queues or workflows)

Autonomy is possible because agents aren’t blind anymore. They know:

• Who they are (persona)

• What to do (next_steps)

• What they’ve done before (memory)

---

Project Setup

We reuse most of the structure from Day 2:

mcp-server/
├── src/
│   ├── index.ts
│   ├── routes/context.ts
│   ├── lib/memoryStore.ts
│   └── types.ts

The only change is how agents use this system — not how it’s served. That’s the beauty of protocol thinking: clients evolve independently.

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Protocol Recap

The agent sends this request:

{
  "agent_id": "research-007",
  "task_id": "mission-04",
  "request_type": "context_bundle"
}

It expects:

{
  "persona": "ResearchGPT",
  "system_prompt": "You are a research agent...",
  "memory": {
    "sources": ["report1.pdf", "report2.pdf"],
    "last_update": "2025-06-20"
  },
  "tools": ["web_search", "summarizer"],
  "next_steps": ["Analyze trends", "Draft summary"]
}

This output helps agents rehydrate their own identity + past state. No need for manual context assembly.

---

Endpoint Overview (routes/context.ts)

No changes needed here — our endpoint already accepts structured agent requests. It was designed with both frontend and agent clients in mind.

But now, we simulate the agent calling it as a standalone process.

---

Agent Code Sample (Autonomous Caller)

Let’s say you have a TypeScript/Node-based agent:

const fetchContext = async () => {
  const res = await fetch("http://localhost:3000/context", {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({
      agent_id: "research-007",
      task_id: "mission-04",
      request_type: "context_bundle"
    })
  });

  if (!res.ok) throw new Error("Failed to fetch context");
  return await res.json();
};

const actOnContext = async () => {
  const context = await fetchContext();
  const prompt = `${context.system_prompt}\n\nMemory:\n${JSON.stringify(context.memory)}\n\nNext Steps:\n${context.next_steps.join("\n")}`;
  // Call OpenAI/Claude here with the prompt
};

The prompt here is reconstructed using:

• system_prompt → core personality

• memory → previous task-related details

• next_steps → chainable future intentions

You can optionally extend this with tool use, decision logs, and state saves.

---

Bootstrapping Agents at Runtime

This simple loop starts a recurring agent:

setInterval(() => {
  actOnContext();
}, 60 * 60 * 1000); // Every hour

Or trigger agents on-demand via HTTP:

app.post("/run-agent", async (req, res) => {
  await actOnContext();
  res.send("Agent run complete");
});

In production systems, this could be part of:

• CRON jobs

• Workflow engines

• Agent spawner services

• Event-driven systems

This structure makes your agent pluggable and composable.

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How Is This Different from Frontend Mode?

The real win? Your agents no longer rely on external context assembly. They become persistent processes that evolve over time.

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What We Achieved

• Gave agents the ability to request context bundles directly

• Simulated an autonomous loop that fetches memory + instructions

• Paved the way for more reactive, modular agent design

Agents now act like autonomous services — not passive responders.

In short, we’ve made our agents aware of:

• Themselves

• Their past

• Their role

• Their tasks

That’s the root of scalable, composable AI systems.

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Up Next: Agent-to-Agent Communication

In Day 4, we’ll go one layer deeper: agents talking to each other using shared context via MCP.

We’ll explore:

• How agents can hand off tasks

• How MCP acts as an inter-agent protocol layer

• What patterns work best for chaining behavior and distributed autonomy

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Protocols first. Prompts second.
Stay tuned for Day 4.