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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Barplot

barplot

Generate a categorical bar chart from a dataset column to visualize category frequencies and return the saved image filename.

Instructions

Generate a categorical bar chart. Args: dataset_id (str): ID of the dataset. column_name (str): Categorical column to visualize. Returns: dict: Dataset ID, column name, and saved image filename.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset_id": "<dataset_id>",
      "column_name": "<column_name>"
    },
    "name": "barplot"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYes
column_nameYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It mentions a saved image filename, implying a file write, but does not disclose output format, file location, overwrite behavior, permissions, or other side effects.

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 concise and front-loaded, with a clear purpose sentence followed by structured Args and Returns sections. It contains no unnecessary wording.

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

Completeness3/5

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

For a simple two-parameter charting tool, the description covers the core purpose, parameters, and return shape. However, it lacks usage guidance relative to sibling visualization tools and omits details about the generated image file.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate. It defines both parameters meaningfully—dataset_id as the dataset ID and column_name as the categorical column to visualize—but adds no constraints, examples, or format details.

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

Purpose4/5

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

The description states a specific verb and resource: "Generate a categorical bar chart." It is clear what the tool does, but it does not differentiate itself from siblings such as countplot or auto_visualize.

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?

There is no explicit guidance on when to use this tool versus alternatives like countplot, boxplot, or auto_visualize. Usage is only implied by the phrase "categorical bar chart."

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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