By Daniel Gudes
Model Context Protocols (MCPs) promised the moon: connect your LLM to real tools and let it take action, live. And yet, in practice, most early rollouts have felt… sluggish. Why? Because raw connectivity isn’t intelligence—and shoving entire API catalogs into a model’s context window doesn’t count as integration.
This post outlines why most MCP agents today fall short, and what it actually takes to build a high-quality, high-ROI integration. We’ll walk through two broken patterns, share battle-tested fixes, and show how we apply those learnings inside AutonomyAI with our TripleR framework.
The MCP Hype Cycle Meets Harsh Reality
When OpenAI and Anthropic launched official MCP support, devs rushed to wire up tool catalogs. But the results have been underwhelming:
- Latency spikes from oversized payloads
- Token overflows from repeated tool listings
- Fragmented planning from poorly structured endpoints
Even Google’s own MCP tutorial warns: “You must pass only the necessary context.”
The core issue? These integrations treat models like terminal operators, not planners. And without smart constraints, you get verbose, wasteful, fragile behavior.
Case Study #1 – The 50-Ticket Dumpster Fire
Imagine an agent calling Linear’s list_issues tool with the default limit: 50 tickets. That alone can chew through 15K+ tokens once the response gets echoed back in JSON.
Fix: Put a hard token budget on each tool. Limit list_issues(limit=8) and chunk requests if needed. OpenAI’s own cookbook recommends this, yet many devs ignore it.
Bonus: expose an estimate_tokens() endpoint so planners can preview call cost.
Case Study #2 – Table Rendering from Hell
Another common anti-pattern: call list_issues, then get_issue N times, then ask the model to reformat all that into a Markdown table.
It’s a guaranteed recipe for context bloat.
Fix:
- Fetch once and cache server-side
- Return a compact ID → field map (or CSV string)
- Let the model reshape data with local code execution (e.g., pandas)
This lets the LLM reason, not babysit payloads.
Good MCP Design Isn’t Optional
Here’s a breakdown of what works—and why:
| Design Guideline | Why It Matters |
|---|---|
| Token-cap every tool | Prevents context explosions |
| Preview/estimate endpoints | Planner can choose efficient paths |
| Aggregated responses | Summarizes large data into model-friendly formats |
| Local code execution | Off-loads manipulation from server |
| Catalog caching | Avoids tool listing on every turn |
If you’re not doing these, you’re not building a real agent—you’re throwing spaghetti at a prompt.
AutonomyAI’s Approach – Why It’s Different
At AutonomyAI, we don’t just connect tools. We structure them.
Each MCP we build—from Figma to ticketing—adheres to internal constraints:
- Token-aware planning
- Aggregated data previews
- Local reasoning workflows
- Context persistence across turns
This feeds into our TripleR framework:
- Retrieval: Pull the right data for each LLM task
- Representation: Transform the data to make it actionable for the LLM, keeping prompts concise while ensuring clarity
- Reuse: Verify that LLMs produce consistent responses to the same prompt across multiple retries.
It’s why our agents work with large codebases, not against them.
Final Thought: Don’t Ship a Showcase—Ship an Agent
Wiring your API into Claude or GPT is easy. Designing for performance, reliability, and context awareness is not.
So before you connect your next tool, ask:
Will this MCP enable real reasoning? Or am I just inflating the prompt?
Done right, MCPs let models act like intelligent collaborators. Done wrong, they’re just over-engineered wrappers for JSON.
Want to see what an intelligent MCP looks like in the wild? Book a demo with AutonomyAI.
#MCP #TripleR #LLMengineering #AutonomyAI #DesignToCode #AgentOps
