MCP vs Tool Use vs LangChain: What to Actually Use

The AI tooling landscape moved fast in 2024. Three patterns emerged for giving LLMs access to external capabilities: Model Context Protocol, native tool use APIs, and agent frameworks like LangChain. Each is legitimate. Each is also overused in situations where the others would be better.

Native tool use

Every major LLM provider now has a tool use API. You define a function with a JSON schema, the model decides when to call it, and you execute it and return the result. That is the whole thing.

Use native tool use when: your tool count is small (under 20), you control the full stack, and you want the smallest possible surface area.

Model Context Protocol

MCP is a standardised protocol for connecting LLMs to external data and tools. The key difference from native tool use: MCP separates the tool definition from the application. A single MCP server can be connected to any compatible client.

Use MCP when: you are building tools that multiple applications or users will consume, you want tool discoverability, or you are building in an ecosystem that is converging on MCP (Anthropic, many IDEs).

LangChain and agent frameworks

Frameworks like LangChain add abstractions on top of tool use: chains, memory, agents with multi-step reasoning loops. They are useful when the coordination logic is complex enough that you want a framework to manage it.

Use a framework when: you are orchestrating multi-agent workflows, you need built-in evals and tracing, or your team velocity matters more than minimising dependencies.

The honest summary

Most production use cases need native tool use. MCP is the right bet for anything ecosystem-facing. Frameworks help when complexity justifies the dependency cost. The mistake is reaching for a framework first and native APIs never.