What Are LLM Tools and Which Ones Do Developers Actually Use?

LLM tools are small, single-purpose utilities that help you build and debug applications on top of large language models. The ones developers reach for most often fall into four groups: token counters, cost calculators, prompt analyzers, and schema converters. HandyAI, for example, lists 10 such tools — token counters, prompt analyzers, schema converters, and cost calculators — described as single-purpose, free, and entirely in-browser, with no sign-up. That combination matters because it tells you what these tools are good for (quick, private, disposable checks) and what they are not (full observability platforms).

The four categories that cover most day-to-day work

Token counting

Token counts decide whether a prompt fits a model's context window and how much a call will cost. HandyAI's Live LLM Token Counter counts tokens for Claude, GPT-4, and Gemini side-by-side with live cost estimates, and shows context sizes such as Claude 3.5 Sonnet at 200K, GPT-4o at 128K, Claude 3 Haiku at 200K, and Gemini 1.5 Flash at 1,000K. Use it before you ship a long system prompt or paste in a large document, so you find the overflow before the API does.

Cost estimation

A token count is only half the answer; the other half is what those tokens cost per call and per month. The AI Cost Calculator projects monthly LLM API costs across Claude, GPT, and Gemini. This is the tool to open when you are choosing between models for a workload, or when you want to know whether a prompt change that adds 2,000 tokens per request is affordable at your call volume.

Prompt analysis and comparison

Prompt quality is hard to see by eye once a prompt grows past a few paragraphs. Two tools in this space are listed as upcoming: the Claude System Prompt Analyzer (Q1 2027), which takes a pasted system prompt and returns clarity, length, and structure feedback, and the Prompt Diff Tool (Q4 2026), which compares two prompt versions structurally so you can see exactly what changed. Until those ship, the practical substitute is disciplined manual versioning plus token counts on each revision.

Schema and format conversion

Wiring a model into tool use means writing a tool definition in exactly the shape the API expects. The JSON Schema → Claude Tool converter (listed for Q3 2026) is meant to turn any JSON Schema into a ready-to-use Anthropic tool-use definition, and Markdown → Anthropic Messages API (Q4 2026) is meant to turn a pasted conversation into the exact request JSON Claude expects. Both are conversion chores that are easy to get subtly wrong by hand.

How to choose, and what to watch for

Three caveats apply across the board. First, coverage is limited: a tool built around Claude, GPT, and Gemini may not know your fine-tuned or self-hosted model. Second, announced tools are not shipped tools — the JSON Schema converter, Cron Expression Explainer, Prompt Diff Tool, Markdown → Anthropic Messages API, Claude System Prompt Analyzer, and Regex Builder from Natural Language all carry future quarters (Q3 2026 through Q1 2027) and a notify-me form, so plan around what exists today. Third, browser-side tools are convenient and keep your prompts local, but they are point checks, not logging or monitoring; for production you still need your own request logging and spend tracking.

A practical workflow

A workable routine for a prompt-heavy project: draft the prompt, run it through the token counter to confirm it fits and to see the per-call cost, adjust length or model choice based on the cost calculator, and keep each version labeled so you can compare them once the diff tool is available. For tool-use work, write the schema first, convert it rather than hand-assembling the definition, and validate the result against a real API call. Combine tools by task rather than expecting one to do everything — the value of single-purpose utilities is that each answers one question quickly, and the cost is that you assemble the workflow yourself.