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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Prediction Plot

prediction_plot

Generate and save an actual-versus-predicted scatter plot for a regression model's test data to visually assess prediction accuracy against the red reference line.

Instructions

Generate and save an actual vs predicted scatter plot for a regression model on its test data. Points close to the red line are good predictions. Args: model_id (str): ID of the trained regression model. Returns: dict: Model ID, number of points plotted, 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": {
      "model_id": "<model_id>"
    },
    "name": "prediction_plot"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_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. It usefully discloses that the tool writes a file to disk ('generate and save') and returns a dict with the model ID, point count, and image filename, plus guidance that points near the red line indicate good predictions. It omits where the image is saved, overwrite behavior, and error handling for invalid or untrained model IDs.

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 purpose sentence is front-loaded and the plot-interpretation note is a helpful one-liner. The 'Args:'/'Returns:' block is docstring boilerplate that partly repeats the schema, but it is short and not distracting.

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 one-parameter visualization tool with no output schema, the description covers the input and the return structure well. However, the saved artifact's location and format are unstated, and there is no coverage of error conditions, which leaves meaningful gaps for a tool whose primary effect is writing a file.

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 coverage is 0%, so the description must compensate, and it does: model_id is explained as the 'ID of the trained regression model,' which adds the important constraint that the model must already be trained and be a regression model. Only one parameter exists, so this covers the input space adequately.

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 an actual vs predicted scatter plot for a regression model on its test data.' The chart type and test-data scope implicitly distinguish it from siblings such as residual_plot or confusion_matrix_plot, but no sibling is named 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 only implied by 'for a regression model on its test data,' which suggests it belongs to post-training evaluation. There is no explicit statement of when to choose this over residual_plot or other diagnostic plots, and no stated prerequisites beyond the model being 'trained.'

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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