Hi, this post brings you real-world MCP production challenges and approaches to solve. Feel free to share additional issues, production experiences, or solutions in the comments.

Madhavi Pasumarthi(#madhaviai) — Mon, 18 May 2026 15:22:42 +0000

Madhavi Pasumarthi(#madhaviai)

May 18

The Real Problems Start After Your MCP Server Works

#ai #mcp #llm

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3 min read

The Real Problems Start After Your MCP Server Works

Madhavi Pasumarthi(#madhaviai) — Mon, 18 May 2026 15:02:13 +0000

I've been spending a lot of time building and deploying MCP servers, experimenting with tool orchestration, agent workflows, and different ways to make LLM systems interact with external systems more reliably.

At first, MCPs feel simple:

Expose tools
Connect an agent
Call functions
Done

But once MCP servers start becoming useful in real workflows, a completely different class of engineering problems begins appearing:

Context explosion
Unreliable tool selection
Hallucinated tool calls
Scaling bottlenecks
Permission boundaries
Non-deterministic agent behavior

This post is not a "how to build an MCP server" guide.

Instead, it's a quick breakdown of the real engineering problems that start appearing after MCPs move to production.

Problem #1 — Too Many Tools Make Agents Worse

One of the biggest MCP engineering problems today is tool overload.

At small scale:
adding tools feels powerful.

At larger scale:

Agent
 ├── 120 tools
 ├── Massive context
 ├── Tool confusion
 └── Lower reliability
     └── Similar tools compete

This is where tool grouping becomes extremely important. Some tools must stay partitioned and selectively loaded. More tools do not automatically create smarter agents, sometimes they create noisier ones.

Fix direction: Group tools by domain and load them selectively per request context. See how GitHub MCP handles tool discovery and scoping github/github-mcp-server/pkg/tooldiscovery. Atlassian Rovo takes a similar approach for Jira + Confluence tool scoping atlassian/atlassian-mcp-server

Problem #2 — Context Windows Become Infrastructure Problems

One thing that becomes obvious very quickly after deploying MCPs is that token usage is no longer just an LLM problem. Every MCP design decision plays a key role:

Tool description
Schema
API payload
Output tokens

Fix direction: Design lean schemas, surface only what the agent needs. See how Stripe's agent toolkit keeps tool payloads focused and minimal stripe/agent-toolkit. The official MCP memory server is also a clean reference for output-efficient tool design modelcontextprotocol/servers/src/memory

Problem #3 — Deterministic Tool Calls Are Hard

One of the biggest misconceptions in MCP systems is:

"If the tool exists, the agent will use it correctly."

In practice, overlapping descriptions, ambiguous naming, and similar tools cause agents to pick incorrect tools surprisingly often.

The real challenge is not making tools callable, it's making the correct tool callable at the correct time.

The tighter and clearer the tool description:

The more deterministic the agent behavior becomes
The fewer hallucinated calls happen
The more reliable orchestration becomes

Fix direction: Write tool descriptions like API contracts, single responsibility, zero ambiguity. Google Gemini CLI's tool definitions folder is a strong reference for well-scoped, clearly named tools google-gemini/gemini-cli/src/tools. Cloudflare MCP also demonstrates clean, single-purpose tool design cloudflare/mcp-server-cloudflare

Problem #4 — MCP Servers Become Distributed Systems

Once MCP traffic grows, MCP servers stop behaving like simple integrations. They start behaving like distributed backend systems.

At production scale:

retries matter,
state management matters,
observability matters,
routing matters,
and horizontal scaling matters.

Client Request
      ↓
Load Balancer
      ↓
Stateless MCP Instance
      ↓
Redis / Session Store
      ↓
External APIs

Fix direction: Build stateless MCP handlers from day one and wire in observability early. Cloudflare Workers MCP shows how to run stateless, edge-deployed MCP instances at scale cloudflare/workers-mcp. GitHub MCP's observability layer is worth studying before you scale github/github-mcp-server/pkg/observability

A Walkthrough of GitHub's MCP Evolution

GitHub discussed how their MCP server gradually evolved while solving:

Huge tool surfaces
Context overload
Scaling problems
Auth workflows
Distributed infrastructure concerns

Their architecture eventually moved toward:

Stateless MCP servers
Redis-backed session handling
Grouped tool sets
Dynamic tooling concepts
OAuth-based auth flows
Scoped tool visibility
Aggressive token optimization

Client
   ↓
MCP Server
   ↓
Tool Sets
   ├── Repo tools
   ├── PR tools
   ├── Actions tools
   └── Issue tools

Final Thoughts

MCP engineering will increasingly depend on:

Strong authentication gateways
Scoped permissions
Tighter tool descriptions
TTL caches
Deterministic orchestration
Context-efficient architectures

DEV Community: Madhavi Pasumarthi(#madhaviai)

Hi, this post brings you real-world MCP production challenges and approaches to solve. Feel free to share additional issues, production experiences, or solutions in the comments.

The Real Problems Start After Your MCP Server Works

The Real Problems Start After Your MCP Server Works

Problem #1 — Too Many Tools Make Agents Worse

Problem #2 — Context Windows Become Infrastructure Problems

Problem #3 — Deterministic Tool Calls Are Hard

Problem #4 — MCP Servers Become Distributed Systems

A Walkthrough of GitHub's MCP Evolution

Final Thoughts