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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Scatterplot

scatterplot

Create and save a scatter plot to visualize the relationship between two numerical dataset columns, returning the dataset ID, column names, and saved image filename.

Instructions

Generate and save a scatter plot to visualize the relationship between two numerical columns. Args: dataset_id (str): ID of the dataset. col_name1 (str): Column for the X-axis. col_name2 (str): Column for the Y-axis. Returns: dict: Dataset ID, column names, 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>",
      "col_name1": "<col_name1>",
      "col_name2": "<col_name2>"
    },
    "name": "scatterplot"
  }
}

Input Schema

NameRequiredDescriptionDefault
col_name1Yes
col_name2Yes
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose two behaviors: the tool saves a file (a side effect) and returns a dict containing the saved image filename. However, it says nothing about where the image is written, format, overwrite behavior, or what happens if a column is non-numerical.

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 core purpose is front-loaded in the first sentence, and the Args/Returns block is compact and each line carries information. The docstring formatting is slightly boilerplate but not wasteful.

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 plotting tool with no annotations and no output schema, the description does cover the return shape (dataset ID, column names, filename). Gaps remain on output location, file format, and failure conditions, which matter for a tool that writes artifacts to disk.

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?

Schema description coverage is 0%, so the description must compensate, and it does: each of the three required parameters is named with its role (dataset_id identifies the dataset, col_name1 is the X-axis, col_name2 is the Y-axis). The added constraint that both columns should be numerical is meaningful semantics the schema does not carry.

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 gives a specific verb and resource ("Generate and save a scatter plot") plus the analytical purpose (relationship between two numerical columns), which clearly separates it from barplot, boxplot, or countplot. It stops short of naming or contrasting any sibling explicitly.

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

Usage Guidelines3/5

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

Usage is implied by "relationship between two numerical columns" — an agent can infer this is for numeric-vs-numeric pairs — but there is no explicit when-to-use, when-not-to-use, or pointer to alternatives such as analyze_correlation or auto_visualize for the same analytic question.

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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