MCP Promised to Fix Agentic AI's Data Problem. Here's What's Still Missing.

Source: Dev.to

The Problem Isn’t MCP. It’s What’s Missing Above It

MCP solved the connection problem, but it introduced new challenges:

Tool Overload

Microsoft researchers found that across 7,000+ MCP servers there are 775 tools with naming collisions—the most common being search. OpenAI recommends keeping tool lists under 20, yet GitHub’s MCP alone ships with ~40. When an LLM is presented with too many tools, it struggles to pick the right one, and performance degrades.

Context Starvation

Even with massive context windows, LLMs can’t efficiently process raw database dumps. Research shows the top MCP tools return an average of 557,766 tokens, enough to overwhelm most models. Agents need relevant data, not all data.

Expensive Tool‑Call Loops

Every tool call is a round‑trip: LLM → client → tool → client → LLM. Each loop includes the full tool list and conversation history. For multi‑step tasks this quickly burns through tokens.

No Intelligent Routing

MCP connects tools to models, but who decides which tool to use? Currently that decision is left to the LLM itself, which isn’t great when facing dozens of options.

The Missing Layer: Semantic Routing

What if there were a layer between the LLM and the MCP tools that:

• Understands intent before selecting tools
• Routes queries to the right data source automatically
• Returns focused data instead of token floods
• Handles multi‑intent queries intelligently

That’s the idea behind OneConnecter.

How It Works

flowchart TD
    User["User: \"weather London Bitcoin price gold futures\""] --> IntentSplitter
    IntentSplitter["Intent Splitter\n(Detects 3 separate intents)"] --> SplitArray["[\"weather London\", \"BTC price\", \"gold futures\"]"]
    SplitArray --> SemanticRouter
    SemanticRouter["Semantic Router\n(Routes each to the right agent)"] --> Agents
    Agents --> Combined["Combined, structured response"]

The LLM never sees 40+ tools; it sees one endpoint that intelligently routes to curated, specialized agents.

Real Results

• 78 % token reduction compared to raw web search (semantic caching)
• Sub‑second routing to the correct data agent
• Clean, structured responses — not HTML dumps or token floods

Example query through the system:

Query: "NVDA stock price market cap"

Response:
- NVDA stock price: $142.50 (+2.3%)
- NVDA market cap: $3.48T

Time: 1.2 s total (including intent split + parallel agent calls)

The intent splitter even knows to duplicate the entity (NVDA) across sub‑queries—something a regex‑based splitter would miss.

The Architecture

┌─────────────────────────────────────────────────────────────┐
│                     OneConnecter                             │
├─────────────────────────────────────────────────────────────┤
│  Intent Splitter    │ Qwen3 4B on Modal (~950 ms)            │
│  Semantic Router    │ Vector embeddings + similarity search │
│  Data Agents        │ Weather, Crypto, Stocks, Commodities   │
│  Semantic Cache     │ Reduces redundant API calls            │
│  MCP Interface      │ Works with Claude, LangChain, etc.     │
└─────────────────────────────────────────────────────────────┘

OneConnecter is MCP‑compatible, so you can plug it into Claude Desktop, LangChain, or any MCP client while solving the problems raw MCP can’t.

Why This Matters

The industry talks about “context starvation” and “tool‑space interference” as if they’re unsolved problems. They’re not. The solution is an intelligent routing layer.

MCP is infrastructure. What we need now is orchestration—something that understands what the user wants and fetches the right data without overwhelming the LLM.

Try It

OneConnecter is live at oneconnecter.io.
Early‑access agents include:

• Real‑time weather data
• Cryptocurrency prices
• Stock market data
• Commodity futures
• Company intelligence
• More agents shipping weekly

If you’re building agentic systems and hitting the walls described above, I’d love to hear from you. Drop a comment or find me on Discord.

What’s Next

• RAG Knowledge Agent — curated scientific/academic data with citations
• More data agents — flights, restaurants, news, jobs
• Better caching — predictive pre‑fetching for common queries

The goal isn’t to replace MCP—it’s to make it actually work in production.

Building OneConnecter at Techne Labs. Follow along as we figure this out.

What problems are you hitting with agentic AI and real‑time data? Let me know in the comments.