ConceptsTool Use & MCP

Enabling Tools on Columns

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This guide explains how to enable tool use on LLM columns by connecting them to tool configurations via the tool_alias parameter.

Overview

To enable tool use on an LLM column, you reference a ToolConfig by its alias. During generation, the model can then request tool calls, and Data Designer executes them and feeds the results back to the model.

Using tool_alias

Add the tool_alias parameter to any supported LLM column configuration:

import data_designer.config as dd
builder.add_column(
    dd.LLMTextColumnConfig(
        name="answer",
        prompt="Use tools as needed to answer: {{ question }}",
        model_alias="nvidia-text",
        tool_alias="my-tools",  # References a ToolConfig
    )
)

Supported Column Types

Tool use is supported on these column configuration types:

Column TypeDescription
LLMTextColumnConfigText generation with tool access
LLMCodeColumnConfigCode generation with tool access
LLMStructuredColumnConfigStructured JSON generation with tool access
LLMJudgeColumnConfigJudge/scoring with tool access

How It Works

When tool_alias is specified:

  1. Tool schemas are fetched from the referenced MCP providers
  2. Model receives tool schemas with the prompt
  3. Model can request tool calls in its response
  4. Data Designer executes calls and returns results to the model
  5. Iteration continues until the model produces a final answer (or limits are reached)

Complete Example

Here’s a complete workflow showing provider → ToolConfig → column:

import data_designer.config as dd
from data_designer.interface import DataDesigner
# 1. Configure MCP provider
mcp_provider = dd.LocalStdioMCPProvider(
    name="demo-mcp",
    command="python",
    args=["-m", "my_mcp_server"],
)
# 2. Create DataDesigner instance with provider
data_designer = DataDesigner(mcp_providers=[mcp_provider])
# 3. Define tool configuration
tool_config = dd.ToolConfig(
    tool_alias="my-tools",
    providers=["demo-mcp"],
    allow_tools=["search_docs", "get_fact"],
    max_tool_call_turns=5,
)
# 4. Create config builder with tool config
builder = dd.DataDesignerConfigBuilder(tool_configs=[tool_config])
# 5. Add columns that use tools
builder.add_column(
    dd.SamplerColumnConfig(
        name="question",
        sampler_type=dd.SamplerType.CATEGORY,
        params=dd.CategorySamplerParams(
            values=["What is machine learning?", "Explain neural networks"]
        ),
    )
)
builder.add_column(
    dd.LLMTextColumnConfig(
        name="answer",
        prompt="Use the available tools to research and answer: {{ question }}",
        model_alias="nvidia-text",
        tool_alias="my-tools",  # Enable tools
        with_trace=dd.TraceType.ALL_MESSAGES,  # Capture tool call history
    )
)
# 6. Generate data
results = data_designer.preview(builder, num_records=5)

See Also