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SmeltCountTool — MCP entry point for smelt count.

SmeltCountTool

Bases: AXMTool

Count tokens in text/data.

Input is resolved in order: caller-provided data, input_path, then non-interactive standard input.

Registered as smelt_count via axm.tools entry point.

Source code in packages/axm-smelt/src/axm_smelt/tools/count.py
Python
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class SmeltCountTool(AXMTool):
    """Count tokens in text/data.

    Input is resolved in order: caller-provided data, ``input_path``, then
    non-interactive standard input.

    Registered as ``smelt_count`` via axm.tools entry point.
    """

    agent_hint: str = (
        "Count tokens in text or data using a specified tiktoken model — "
        "returns token count without modifying input"
    )

    @property
    def name(self) -> str:
        """Return tool name for registry lookup."""
        return "smelt_count"

    def execute(
        self,
        *,
        data: JsonValue = "",
        input_path: str | None = None,
        model: str = "o200k_base",
        **kwargs: object,
    ) -> ToolResult:
        """Count tokens in data.

        Args:
            data: Text or data to count tokens for.
            input_path: Optional UTF-8 input file used when data is empty.
            model: Tiktoken model name.

        Returns:
            ToolResult with token count and model used.
        """
        try:
            if data is None:
                msg = "data must not be None"
                raise ValueError(msg)

            if isinstance(data, str):
                data = resolve_text_source(data, input_path, stdin=sys.stdin)

            if not isinstance(data, str):
                # Reuse the pipeline's canonical serialization
                # (separators=(",", ":") + sort_keys=True) so smelt_count
                # measures the same baseline that smelt later tokenizes.
                from axm_smelt.core.models import SmeltContext

                data = SmeltContext(parsed=data).text

            from axm_smelt.core.counter import count_with_backend

            tokens, backend = count_with_backend(data, model=model)

            return ToolResult(
                success=True,
                data={
                    "tokens": tokens,
                    "model": model,
                    "counter_backend": backend.value,
                },
                text=(
                    f"smelt_count | {tokens} tokens | {len(data)} chars | "
                    f"{model} | {backend.value}"
                ),
            )
        except Exception as exc:  # noqa: BLE001
            return ToolResult(success=False, error=str(exc))
name property

Return tool name for registry lookup.

execute(*, data='', input_path=None, model='o200k_base', **kwargs)

Count tokens in data.

Parameters:

Name Type Description Default
data JsonValue

Text or data to count tokens for.

 ''
input_path str | None

Optional UTF-8 input file used when data is empty.

 None
model str

Tiktoken model name.

 'o200k_base'

Returns:

Type Description
ToolResult

ToolResult with token count and model used.
Source code in packages/axm-smelt/src/axm_smelt/tools/count.py
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def execute(
    self,
    *,
    data: JsonValue = "",
    input_path: str | None = None,
    model: str = "o200k_base",
    **kwargs: object,
) -> ToolResult:
    """Count tokens in data.

    Args:
        data: Text or data to count tokens for.
        input_path: Optional UTF-8 input file used when data is empty.
        model: Tiktoken model name.

    Returns:
        ToolResult with token count and model used.
    """
    try:
        if data is None:
            msg = "data must not be None"
            raise ValueError(msg)

        if isinstance(data, str):
            data = resolve_text_source(data, input_path, stdin=sys.stdin)

        if not isinstance(data, str):
            # Reuse the pipeline's canonical serialization
            # (separators=(",", ":") + sort_keys=True) so smelt_count
            # measures the same baseline that smelt later tokenizes.
            from axm_smelt.core.models import SmeltContext

            data = SmeltContext(parsed=data).text

        from axm_smelt.core.counter import count_with_backend

        tokens, backend = count_with_backend(data, model=model)

        return ToolResult(
            success=True,
            data={
                "tokens": tokens,
                "model": model,
                "counter_backend": backend.value,
            },
            text=(
                f"smelt_count | {tokens} tokens | {len(data)} chars | "
                f"{model} | {backend.value}"
            ),
        )
    except Exception as exc:  # noqa: BLE001
        return ToolResult(success=False, error=str(exc))