The Model Context Protocol (MCP) was supposed to be the universal connector for agentic AI — a standard way for agents to call tools without custom glue code. But at Ask 2026, Perplexity CTO Denis Yarats dropped a significant signal: Perplexity is moving away from MCP internally, and the reason has major implications for anyone building production agentic systems.

The Problem: 55,000 Tokens Before Your Agent Does Anything

Yarats was direct about the technical issue. MCP tool definitions — the schema declarations that tell an agent what tools are available and how to call them — were consuming 55,000+ tokens before a single user message was processed.

That’s not a rounding error. At current frontier model pricing, burning 55,000 tokens on protocol overhead before your agent takes its first action can represent a significant portion of total inference cost for routine tasks. More importantly, it compresses the effective context window available for the actual work the agent needs to do.

The secondary problem Yarats cited: authentication friction. MCP’s auth model adds complexity to deployment and maintenance that teams building internal tooling often don’t want to absorb.

Multiple independent sources corroborate the scale issue. Cloudflare and developer Julien Simon have both noted MCP’s token scaling problem in published technical analyses.

What Perplexity Is Doing Instead

The announcement doesn’t mean Perplexity is abandoning tool-calling — far from it. They’re moving to a leaner integration pattern that avoids front-loading the full tool schema into every request context.

This is consistent with a broader pattern of engineering teams discovering that progressive disclosure — revealing tool capabilities as needed rather than dumping the full manifest upfront — dramatically reduces context overhead.

Apideck’s 80-Token Alternative

While Perplexity announced the problem, Apideck built a concrete solution. Their CLI alternative to MCP uses ~80-token agent prompts with progressive disclosure — a 687× reduction compared to a full MCP tool definition set.

The approach: instead of front-loading all tool schemas, the agent starts with a minimal capability description and fetches specific tool details only when it decides to use them. It trades a small latency increase per tool call for a massive reduction in base context overhead.

For teams where most agent runs use only 2-3 tools out of a potential 50+, the math is obvious: don’t load 47 tool schemas you’re never going to call.

What This Means for Your Agentic Stack

MCP is still useful — especially for standardizing tool interfaces across teams and projects. But these developments suggest a nuanced view is warranted:

MCP works well when:

  • You have a small, fixed set of tools (under ~10)
  • The context overhead is negligible relative to your task complexity
  • You need cross-team standardization more than you need token efficiency

Consider alternatives when:

  • Your tool set is large (50+ tools)
  • You’re running high-volume agents where per-call cost matters
  • Most agent runs only need a small subset of available tools
  • You’re working with tight context windows or latency constraints

The Perplexity signal is significant because they operate at scale with real production constraints — not a toy research project. When a company at that level publicly moves away from a protocol, it’s worth taking the underlying technical concern seriously.

The MCP ecosystem will likely respond with optimizations — tool chunking, lazy-loading schemas, compressed representations. But for teams deploying agents today, the 55,000-token warning deserves attention.

Sources

  1. Agent Engineering — Why Perplexity Is Stepping Back from MCP Internally
  2. Julien Simon (Medium) — Still Missing Critical Pieces
  3. Apideck — MCP Server Eating Context Window: CLI Alternative

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