Tool Loadout

Problem

Function-calling accuracy falls off sharply once the model is shown more than roughly twenty tool definitions at once: the model picks the wrong tool, mixes up similarly named ones, or ignores the right tool entirely. Worse, every irrelevant tool definition still consumes context tokens on every call. Exposing the full registry to the main inference is effectively unusable past a certain size, and a static loadout cannot adapt to per-request intent.

Solution

Before the main loop, classify the request and select N relevant tools (rule-based: by routed lane; or model-based: a quick classifier picks tools). Expose only the selected subset to the agent's main inference call. Tools outside the subset are unavailable for this request.

When to use

• The tool registry is large (MCP, plugins, internal catalog) and exposing all degrades selection.
• A classifier or rule can pick the relevant subset per request cheaply.
• Function-calling accuracy is a release-gate metric.