Same AI code, two outcomes: what actually makes a tool 'agentic'

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A developer noticed that two tools in their repository — a manual Python script and an MCP server tool — ran identical AI code to generate git commit messages, yet only one was truly 'agentic'. The key difference was a single decorator, @mcp.tool(), which converts a Python function's signature into a readable JSON schema that an AI agent can discover and reason about before calling it. The manual script, by contrast, is invisible to agents — it has no schema, no discoverability, and can only be used by a human who already knows it exists. The distinction became practical when an agent trying to use the script via a generic bash tool couldn't reliably interpret its human-facing error messages and exit codes. The takeaway is that agenticism is not about whether an LLM is involved, but whether a tool exposes a structured, machine-readable interface that an agent can act on autonomously.

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