MCP Is Just Libraries Repackaged: Déjà Vu All Over Again

A Reddit post on r/LocalLLaMA references an article titled "The MCP Era Feels Like Déjà Vu" that argues Anthropic will soon realize that MCP (Model Context Protocol) is basically programming libraries repackaged. The article uses Hugging Face's smolagents tool design as a concrete example to show how a tool is essentially what any package function does.

Key Arguments

• Tools are functions: The smolagents design treats tools as callable functions, which is functionally identical to importing a library and calling a function. The only difference is LLM-friendly documentation and standardized input/output schemas.
• MCP = library of documented functions: The author argues that an MCP server exposing a set of tools is equivalent to a library of functions, but with descriptions and schemas optimized for LLM consumption. There's no fundamental architectural innovation — it's just old concepts with a new coat of paint.
• Practical question for the community: Should we invest in building a new MCP ecosystem, or should we focus on improving documentation of existing libraries, removing bad implementations and redundant packages? The author leans toward the latter.

Community Reactions

The Reddit discussion expands on this, with users pointing out that MCP does standardize how models discover and call tools, which is a step up from ad-hoc API schemas. Critics argue that the real value is in consistency, not novelty. Some suggest MCP could evolve into a better version of OpenAPI for AI agents, but caution against over-engineering.

Takeaway for Developers

The core insight for anyone building with AI agents: think carefully before jumping on MCP. If your tool needs are simple, a well-documented Python library with clear function signatures might be all you need. MCP may add unnecessary complexity for marginal gains. Conversely, if you need dynamic tool discovery across multiple services, MCP's standardization could be worth the overhead.