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Linear Regression MCP

Linear Regression MCP

by HeetVekariya
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_columns_info

Retrieve column names from uploaded CSV files to understand dataset structure for linear regression analysis.

Instructions

This function gives information about columns.

Returns: String which contains column names.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_columns_info"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • server.py:69-81 (handler)
    The main handler function for the 'get_columns_info' tool. It is decorated with @mcp.tool() for registration in the FastMCP server and retrieves the column names from the shared DataContext, joining them into a comma-separated string.
    @mcp.tool()
def get_columns_info() -> str:
    """
    This function gives information about columns.

    Returns:
        String which contains column names.
    """

    columns = context.get_data().columns

    return ", ".join(columns)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.8/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It states the tool returns a string with column names, which is basic behavioral information, but doesn't disclose important traits like whether this is a read-only operation, performance characteristics, error conditions, or what format the string uses (e.g., comma-separated, JSON). For a tool with zero annotation coverage, this is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is brief and structured with a purpose statement followed by return information. However, the second sentence 'Returns: String which contains column names.' could be integrated more smoothly, and there's some redundancy in stating 'This function gives information about columns' then specifying the return. It's efficient but not perfectly polished.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and a simple zero-parameter tool, the description is incomplete. It mentions the return is a string with column names, but doesn't explain what context or data source this applies to, how the string is formatted, or any limitations. For even a simple tool, more context about behavior and output is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, and schema description coverage is 100% (though trivial since there are no parameters). The description doesn't need to explain parameters, and the baseline for zero parameters is 4. No additional parameter semantics are needed or provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'gives information about columns' which is a clear purpose, but it's vague about what specific information is provided. It distinguishes from siblings like 'check_category_columns' or 'label_encode_categorical_columns' by focusing on general column information rather than specific transformations, but doesn't explicitly differentiate itself.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided about when to use this tool versus alternatives. The description doesn't mention any context, prerequisites, or comparisons to sibling tools like 'check_category_columns' which might also provide column information. The agent must infer usage from the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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