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EnglishWhat Are Claude Tools and Which Ones Do Developers Actually Use?
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Claude tools are small, single-purpose utilities that help developers work with Anthropic's Claude models — counting tokens, estimating API cost, converting JSON Schema into a tool-use definition, and auditing prompts. On HandyAI, a set of these tools runs entirely in the browser with no sign-up, and several more (schema conversion, prompt diffing, system prompt analysis) are listed as upcoming. You reach for them when you need a quick, verifiable number or a correctly formatted payload before you send a real request to the Claude API.
The four categories that matter
Claude-specific developer utilities cluster into four jobs. Each answers a different question you'd otherwise answer by trial and error against the API.
| Tool type | Question it answers | When to reach for it |
|---|---|---|
| Token counter | How many tokens is this text, and what will it cost per call? | Before sending a prompt, to check you're inside the context window and to budget |
| Cost calculator | What will my monthly API spend look like across models? | When choosing between Claude models or projecting a bill |
| JSON Schema → Claude tool-use converter | How do I express this schema as an Anthropic tool definition? | When wiring up function calling / tool use |
| Prompt analyzer | Is my system prompt clear, and how long is it? | When a prompt underperforms or you're trimming length |
The distinction from general LLM tools is scope: a token counter that only handles one model tells you less than one that shows Claude, GPT, and Gemini side by side, because model choice changes both tokenization and price.
Token counting and cost estimation before you send a request
The live token counter on HandyAI is the entry point. You paste text, and it reports characters, words, and token counts for several models at once — the page lists Claude 3.5 Sonnet (200K context), Claude 3 Haiku (200K context), GPT-4o (128K context), and Gemini 1.5 Flash (1,000K context) — with a per-call cost estimate next to each.
The practical workflow:
- Paste the exact prompt you intend to send, including system instructions and any few-shot examples.
- Read the token count against the context window shown for your target model. If you're near the limit, trim examples or split the task.
- Compare the per-call cost across models. A prompt that's cheap on Haiku may be several times the price on Sonnet — the side-by-side view makes that visible before you commit.
- Multiply by expected call volume to sanity-check the monthly figure, or use the separate AI Cost Calculator, which projects monthly LLM API costs across Claude, GPT, and Gemini.
The reason to do this client-side rather than by calling the API is speed and cost: you get an answer without spending tokens or waiting on a round trip. Treat the numbers as estimates — tokenization details and current per-token pricing should be confirmed against Anthropic's own documentation before you rely on them for a budget.
Converting JSON Schema into an Anthropic tool-use definition
Tool use (function calling) requires you to describe each tool's inputs in a specific shape that Claude expects. If you already have a JSON Schema — from an OpenAPI spec, a validation library, or an internal service — rewriting it by hand into an Anthropic tool definition is mechanical and easy to get subtly wrong.
The JSON Schema → Claude Tool converter is designed for exactly this: paste a JSON Schema, get a ready-to-use Anthropic tool-use definition. As of the site's listing it is marked for Q3 2026 and not yet live; the page offers an email notification and points you to the token counter in the meantime. Until it ships, the manual path is to map your schema's properties, required, and type fields into the tool's input schema and keep descriptions short and specific, since Claude uses them to decide when to call the tool.
Prompt analysis and the tools still coming
The Claude System Prompt Analyzer (listed for Q1 2027) is aimed at the clarity, length, and structure of a system prompt — useful when a prompt is long enough that token cost and instruction-following both suffer. Two other upcoming tools round out the set: a Prompt Diff Tool (Q4 2026) for comparing two prompt versions structurally, and Markdown → Anthropic Messages API (Q4 2026) for turning a pasted conversation into the exact request JSON Claude expects.
If your immediate need is a number — tokens, cost, context headroom — the live token counter and cost calculator cover it today. If your need is a correctly formatted payload for tool use or the Messages API, check whether the converter has shipped; otherwise plan to hand-write the definition and validate it with a test call.
Choosing the right tool for the task
- Checking whether a prompt fits: token counter, compared against the model's context window.
- Deciding between Claude models: token counter's per-call estimates, then the cost calculator for monthly projection.
- Setting up function calling: the JSON Schema converter once available; manual mapping until then.
- Diagnosing a weak or bloated system prompt: the system prompt analyzer once available; otherwise count tokens first and cut length.
All of the live tools described here run in-browser and require no sign-up, which makes them safe for pasting prompt text you'd rather not send to a third-party server — but confirm that client-side claim yourself for anything sensitive, and verify current model names, context limits, and pricing against Anthropic's documentation, since those change independently of any third-party tool.