toolpick

Your agent has 30 tools. The model sees all of them on every step — tool definitions eat thousands of tokens, the model picks the wrong one, and you're paying for context it doesn't need.

toolpick fixes this. It picks the right 5 tools per step so the model only sees what matters.

npm install toolpick

import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
import { createToolIndex } from "toolpick";

const index = createToolIndex(allTools, {
  embeddingModel: openai.embeddingModel("text-embedding-3-small"),
});

const result = await generateText({
  model: openai("gpt-4o"),
  tools: allTools,
  prepareStep: index.prepareStep(),
  prompt: "ship it to prod",
});

Every step, toolpick selects the most relevant tools and sets activeTools automatically. The model only sees what it needs. All tools remain available for execution.

Works with generateText, streamText, and Experimental_Agent.

Pass an embedding model and toolpick indexes your tool names, descriptions, and parameters. On each step it runs a combined search — fast keyword matching plus semantic embeddings — and returns the best matches.

If the model can't find a useful tool, the next step automatically pages to a fresh set it hasn't seen yet. Two misses in a row? All tools get exposed as a fallback. Your agent never gets stuck.

If the embedding API goes down, it silently falls back to keyword-only search. No crashes.

For maximum accuracy, add a rerankerModel. The combined search fetches candidates, then a cheap LLM picks the best ones using reasoning — handling slang, abbreviations, and context that embeddings alone miss.

const index = createToolIndex(allTools, {
  embeddingModel: openai.embeddingModel("text-embedding-3-small"),
  rerankerModel: openai("gpt-4o-mini"),
});

This takes accuracy from 84% to 100% on blind user queries. Cost: ~$0.0001 per step (gpt-4o-mini).

For an extra boost, enrichDescriptions expands your tool descriptions with synonyms and alternative phrasings at startup. This is a one-time LLM call during warmUp():

const index = createToolIndex(allTools, {
  embeddingModel: openai.embeddingModel("text-embedding-3-small"),
  rerankerModel: openai("gpt-4o-mini"),
  enrichDescriptions: true,
});

await index.warmUp();

For agents that need to discover tools outside the current selection, expose the built-in search tool:

const result = await generateText({
  model: openai("gpt-4o"),
  tools: {
    ...allTools,
    search_tools: index.searchTool(),
  },
  prepareStep: index.prepareStep({ alwaysActive: ["search_tools"] }),
  prompt: "find and use the right tool",
});

The model can call search_tools to browse the full catalog and request specific tools be activated. This mirrors how Anthropic's tool search works — but for any model.

Tool description embeddings are computed once at startup. Cache them to skip the API call on restarts:

import { createToolIndex, fileCache } from "toolpick";

const index = createToolIndex(allTools, {
  embeddingModel: openai.embeddingModel("text-embedding-3-small"),
  embeddingCache: fileCache(".toolpick-cache.json"),
});

await index.warmUp();

Tested with 19 blind user queries — slang, vague language, no keyword overlap — against 30 real SaaS tools:

Queries like "ship it", "ping the team", "file a bug", "get the CI running", and "compress this photo" all resolve to the correct tool.

const index = createToolIndex(tools, {
  embeddingModel: model,          // enables combined search (recommended)
  rerankerModel: model,           // LLM re-ranking for max accuracy
  enrichDescriptions: true,       // expand descriptions with synonyms at warmUp
  embeddingCache: fileCache(path), // persist embeddings to disk
  strategy: "hybrid",             // force keyword-only (free, no API calls)
});

await index.select(query, {
  maxTools: 5,                    // ceiling, not fixed count
  adaptive: true,                 // cut early when scores drop off
  alwaysActive: ["search_tools"], // always include these
  threshold: 0.3,                 // minimum relevance score
});

MIT