MCP was the wrong abstraction for AI agents (getateam.org)

Anthropic’s Model Context Protocol (MCP) promised a universal tool layer for agents, but GetATeam’s tests show it fails for data‑heavy production workflows. MCP implementations commonly load dozens of tool definitions (e.g., 6 servers × 25 tools ≈ 150 tools → 15k–30k token overhead) and return large intermediate results into the LLM context (e.g., 50k‑token transcripts), producing massive token bloat, higher latency, higher cost and more hallucinations. In GetATeam’s benchmarks (Claude 3.5 Sonnet, 10 runs), an MCP version consumed ~87k tokens and took 45s vs a code‑execution “skills” agent that used ~1.8k tokens and 12s — a ~98% token and cost reduction and better output quality. MCP still makes sense when the task requires full‑document semantic reasoning or when you need cross‑vendor standardization. The alternative: agents discover, import and run typed skill functions (TypeScript) that fetch/store data off‑context (filesystem, DB) and return only the necessary bytes to the model. This enables progressive disclosure, stateful skill evolution (agents can write and persist new skills), stronger privacy (anonymization harnesses), and near‑unlimited tool scale via search/semantic indexing. Security is addressed with VM isolation, capability‑based permissions, AST/code pattern validation, timeouts and strict cleanup/auditing. Bottom line: for production, data‑intensive agents, code‑execution skills outperform MCP on cost, latency, autonomy and privacy; MCP’s value persists for broad ecosystem interoperability.