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

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:

Pattern	Description
Stateless Agents	Agents don’t carry memory — they ask for it
Context Bundles	Agents receive rich, structured state: who, what, why, and how
Autonomous Loops	Agents can wake up, pull context, act, and repeat
Agent Delegation	Agents leave instructions for others inside the shared memory
Chain of Roles	Planner → Researcher → Writer → QA → Deployer, all mediated by MCP
Runtime Modularity	Swap out agents, tools, or memory engines with no architecture rewrite

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.