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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Export Dataset

export_dataset

Exports a dataset to CSV, Excel, JSON, Parquet, or pickle and writes it to a specified file name, returning the output path, format, and row/column counts.

Instructions

Export a dataset to CSV, Excel, JSON, Parquet or pickle. Args: dataset_id (str): ID of the dataset. extension_name (str): One of csv, json, xls, xlsx, parquet, pkl. file_name (str): Output file name or path. The extension is added if it is missing, e.g. "cars" -> "cars.csv". Returns: dict: The path that was written, the format and the number of rows and columns.

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>",
      "extension_name": "<extension_name>",
      "file_name": "<file_name>"
    },
    "name": "export_dataset"
  }
}

Input Schema

NameRequiredDescriptionDefault
file_nameYes
dataset_idYes
extension_nameYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations present the description carries the full load, and it does disclose the extension auto-append behavior ("cars" -> "cars.csv") and the return payload of path, format, and row/column counts. It says nothing about whether an existing file is overwritten, which directory relative paths resolve to, or permissions/disk side effects — meaningful gaps for a write-to-disk operation.

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

Conciseness5/5

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

The opening sentence front-loads the purpose and formats, and the Args/Returns blocks are compact with no filler. Every line carries information an agent needs, including the extension rule that prevents a malformed filename.

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

Completeness4/5

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

For a three-parameter write tool with no annotations and no output schema, the description covers parameters and return shape adequately. The remaining gap is the destructive/side-effect profile (overwrite semantics and target directory), which matters because the tool writes files.

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: all three required parameters are named with types, extension_name enumerates the accepted values (csv, json, xls, xlsx, parquet, pkl), and file_name's extension-appending rule is spelled out with an example. What's missing is whether file_name accepts a full path or only a basename, which the schema also leaves open.

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 an explicit verb ('Export') and resource ('a dataset') plus the supported output formats, so the agent knows precisely what the tool does. It doesn't distinguish itself from nearby siblings such as copy_dataset, but the operation is unambiguous on its own.

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: the tool exists to serialize a dataset to disk, and the format list hints at the choice the caller must make. There is no statement of when to prefer this over copy_dataset, no prerequisites, and no exclusions, so guidance remains minimal.

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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