How Does Claude Use Tools? The Mechanics Explained

Claude doesn't "run" tools itself. It reads a list of tool definitions you send with your request, decides — based on the conversation — whether calling one would help answer the user, and if so, responds with a structured request telling you which tool to call and with what arguments. Your application then executes the actual function (hitting an API, querying a database, running code) and sends the result back to Claude, which uses it to write the final answer.

That's the whole mechanism: Claude never executes code or touches the network directly. It's a language model that's been trained to recognize when a task requires external information or action, and to express that need in a predictable JSON format instead of guessing. Everything else — the actual execution — is your responsibility as the developer.

The Core Loop

Every tool-using conversation with Claude follows the same pattern:

1. You send a message plus a tools array describing each tool's name, purpose, and expected input schema.
2. Claude reads the user's message and the tool descriptions, then decides whether a tool is needed.
3. If yes, Claude stops generating text and returns a tool_use block containing the tool name and structured arguments.
4. Your code runs the real function using those arguments.
5. You send the result back to Claude as a tool_result block in a follow-up message.
6. Claude reads the result and continues — either answering directly or calling another tool.

This can loop multiple times in a single turn if the task requires chaining tools together, like looking up a customer ID before fetching their order history.

What a Tool Definition Actually Tells Claude

Claude has no built-in knowledge of your database schema, your internal APIs, or your business logic. It only knows what you describe in the tool's JSON schema. A typical definition includes:

{
  "name": "get_weather",
  "description": "Get the current weather for a given city",
  "input_schema": {
    "type": "object",
    "properties": {
      "city": { "type": "string", "description": "City name, e.g. Paris" },
      "unit": { "type": "string", "enum": ["celsius", "fahrenheit"] }
    },
    "required": ["city"]
  }
}

Claude uses the description fields to decide when the tool is relevant and what to put in each argument. Vague descriptions lead to vague or wrong tool calls — this is why precise, example-rich descriptions matter more than clever prompting.

How Claude Decides to Call a Tool

Claude weighs three things when deciding whether to use a tool:

• Necessity — can it answer confidently from what it already knows, or does it need fresh, external, or computed data?
• Fit — does any available tool's description match the need closely enough?
• Sufficiency of arguments — does it have enough information from the conversation to fill the required parameters, or should it ask the user first?

If Claude lacks a required argument (say, a city name for a weather tool), it will typically ask a clarifying question instead of guessing. This is a training behavior, not a hard rule, so ambiguous schemas or overlapping tool descriptions can still cause incorrect calls — which is why testing tool definitions with real prompts matters more than writing them once and forgetting them.

A Full Example Request/Response Cycle

Here's what the raw exchange looks like when calling Claude directly:

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-sonnet-4",
    "max_tokens": 1024,
    "tools": [{
      "name": "get_weather",
      "description": "Get current weather for a city",
      "input_schema": {
        "type": "object",
        "properties": { "city": { "type": "string" } },
        "required": ["city"]
      }
    }],
    "messages": [
      { "role": "user", "content": "Should I bring an umbrella in Lisbon today?" }
    ]
  }'

Claude's response won't contain a final answer yet — it'll contain a tool_use block:

{
  "stop_reason": "tool_use",
  "content": [
    {
      "type": "tool_use",
      "id": "toolu_01A",
      "name": "get_weather",
      "input": { "city": "Lisbon" }
    }
  ]
}

Your code calls the real weather API, then sends the result back:

{
  "role": "user",
  "content": [{
    "type": "tool_result",
    "tool_use_id": "toolu_01A",
    "content": "Rain expected, 22°C"
  }]
}

Claude then writes the final natural-language answer using that data. This request/response structure is identical whether you're calling the Anthropic API directly or through a proxy like SubToAPI's /v1/messages endpoint — see the Messages API docs and the tool use docs for the full schema.

Streaming and Tool Use Together

When streaming is enabled, tool calls arrive as partial JSON deltas rather than one complete block, which lets you show "Claude is thinking..." or partial argument construction in a UI before execution starts. This adds complexity to parsing but doesn't change the underlying loop — you still wait for the full tool_use block before running the tool. Details are in the streaming docs.

Why This Matters for Builders

Understanding this mechanism changes how you design tools. Since Claude only "knows" what's in the schema and description, the biggest source of unreliable tool use isn't the model — it's poorly specified tools. Clear names, tight schemas, and explicit descriptions of when not to use a tool all measurably improve accuracy.

If you're building on top of Claude and want a single API key, usage dashboards, and team billing instead of juggling raw Anthropic credentials across projects, SubToAPI wraps the same Messages and tool-use behavior behind sub_live_... keys — see the quickstart guide or pricing to get started.

FAQ

Does Claude execute tools itself? No. Claude only outputs a structured request describing which tool to call and with what arguments. Your application executes the actual function and returns the result.

Can Claude call multiple tools in one turn? Yes. Claude can issue several tool_use blocks in sequence or in parallel within a turn, chaining results together before producing a final answer.

What happens if Claude picks the wrong tool? Usually it means the tool's description or schema was ambiguous. Tightening the description, adding examples, or splitting overlapping tools into more specific ones typically fixes it.