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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Load Parquet

load_parquet

Load a Parquet file from disk into a new dataset and return its dataset ID, row count, column count, and column names.

Instructions

Load a Parquet file from disk into a new dataset. Args: file_path (str): Full path to the Parquet file on the machine running the server. Returns: dict: dataset_id, rows, columns and column_names of the new dataset.

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": {
      "file_path": "<file_path>"
    },
    "name": "load_parquet"
  }
}

Input Schema

NameRequiredDescriptionDefault
file_pathYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full burden. It does disclose a genuinely useful behavioral detail — the path is resolved on the machine running the server, not the client — and it states what the call returns (dataset_id, rows, columns, column_names). It says nothing about error behavior for nonexistent/unreadable files, size limits, or whether the new dataset persists across sessions.

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 remaining lines are functional. The 'Args:'/'Returns:' docstring formatting is slightly rigid for a one-parameter tool but every line carries information, so there is little waste.

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 single-parameter loader with no output schema, the description covers the essential contract: the input path semantics and the shape of the returned dict. Missing failure-mode details are a minor gap given the tool's simplicity, so the definition is essentially complete for an agent to call it correctly.

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 define the single parameter, and it does so meaningfully: 'Full path to the Parquet file on the machine running the server' clarifies both that a complete absolute path is expected and whose filesystem it refers to. That is real added meaning beyond the bare {"file_path": "string"} schema; only format expectations (file extension) are left implicit.

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 ('Load a Parquet file from disk into a new dataset'), which cleanly identifies the operation and implicitly distinguishes it from format-specific siblings like load_csv or load_excel. It stops short of explicitly naming those siblings as alternatives, so it is clear but not sibling-differentiating.

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 the file format (Parquet) and by the 'load ... into a new dataset' framing, which tells the agent to use this when a Parquet file needs materializing as a dataset. However, there is no explicit when-to-use/when-not guidance, no mention of what happens if the file is missing or malformed, and no reference to the other loaders as alternatives.

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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