MCP Server Wrap-Up — Patterns, Libraries & Scaling Context

This content originally appeared on DEV Community and was authored by Codanyks

A final look at how to scale agent memory, build coordination patterns, and extend MCP into real-world systems.

Let’s Recap the Journey

We started with a problem every agent-based developer faces:
Memory is duct-taped. Context is fragile. Coordination is chaotic.

Then, over 4 days, we built and evolved a system to change that.

Day 1 — What is MCP Server?

• Identified the “context crisis” in LLM systems

• Introduced the Model Context Protocol as a solution

• Showed how a centralized MCP server can manage memory, goals, identity, and tool routing

Day 2 — Built an MCP Server

• Implemented a TypeScript server that delivers structured context

• Introduced a clean request/response protocol

• Designed for frontend → MCP → agent architecture

Day 3 — Agents Fetch Their Own Context

• Agents became autonomous context consumers

• Enabled polling loops, cron tasks, and background agents

• Created the foundation for modular, runtime-resilient agent design

Day 4 — Agents Talk to Each Other via MCP

• Introduced indirect agent-to-agent communication

• Agents read/write shared memory without direct messaging

• Built coordination patterns like chain-of-thought, delegation, and swarm behavior

Putting the Architecture Together

At the end of this series, here’s the big-picture model:

+-------------+       +-------------+       +-------------+
|  Frontend   | <---> |   MCP API   | <---> |   Agents    |
+-------------+       +-------------+       +-------------+
                           |
                           v
                +-----------------------+
                | Memory + Goal Stores  |
                +-----------------------+

• MCP is the context router, memory librarian, identity resolver, and goalkeeper.

• Agents are stateless functions that think clearly because they ask for the right input.

• Frontend/UI is optional — orchestration logic can live anywhere.

Patterns We Observed

Here’s what emerged naturally as you followed the protocol mindset:

Libraries & Tools That Pair Well

Want to extend your MCP ecosystem? Here are some options:

Memory & State

• Redis — for fast task state or ephemeral context

• PostgreSQL — for structured goal + agent metadata

• LiteFS — for distributed SQLite across edge agents

LLM Agents

• OpenAI, Claude, Gemini — use your model of choice

• CrewAI, AutoGen, LangGraph — use MCP to feed context into these frameworks

API & Infra

• http-error-kit + @wthek/express-middleware — clean error handling for your TypeScript MCP server

• zod + @wthek/zod-express-interceptor — schema-level request validation

• Postman — for quick testing MCP routes

Going Beyond the Series

Now that you’ve built your MCP system:

• Add tool routers: Let MCP tell agents what tools they can use

• Layer in feedback loops: Save model outputs into memory for the next round

• Enable multi-tenant memory: Separate goal trees by user, product, or agent persona

• Visualize workflows: Turn your context requests into event graphs

Final Guidance: Design Like a Protocol, Not a Pipeline

Here’s the key takeaway:

Don’t hardcode behavior.
Don’t duct-tape prompts.
Don’t make your agent “smart” by making the prompt longer.

Instead:

Design interfaces
Serve structured context
Let agents be actors — and MCP be the stage manager

That’s how you scale weirdness, not chaos.
That’s how you build systems that grow without growing fragile.

Thanks for Reading

If you’ve followed the whole series, you now have the mindset and tools to build your own agent architecture from scratch — or retrofit MCP into an existing one.

We’ll keep building. You should too.
Keep it weird. Keep it modular. Keep it scalable.

Protocols first. Prompts second.

This content originally appeared on DEV Community and was authored by Codanyks