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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Load Datasets Into Dataframe

load_datasets_into_dataframe

Load .csv, .xls, .xlsx, .json, or .parquet datasets into a pandas DataFrame from uploaded file bytes, URLs, or file paths to prepare data for analysis.

Instructions

Load a dataset into a pandas DataFrame from uploaded file bytes or from a URL / file path. Supported extensions: .csv, .xls, .xlsx, .json, .parquet. To load a file that is on the machine running the server, prefer load_csv / load_excel / load_json / load_parquet. Args: file (bytes, optional): Raw file content. Needs file_name so the format is known. file_name (str, optional): Name of the file (its extension selects the reader). dataset_url (str, optional): URL (or path) to read from. If the URL has no extension, pass file_name. Returns: dict: dataset_id, rows, columns and column_names of the new dataset.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "load_datasets_into_dataframe"
  }
}

Input Schema

NameRequiredDescriptionDefault
fileNo
file_nameNo
dataset_urlNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the supported format set, the conditional dependency that file_name is required alongside file, the fallback for extensionless URLs, and the returned dict keys. It omits side-effect and failure context — that a new dataset_id is created/persisted, and what happens on unsupported extensions or unreachable URLs.

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?

Front-loaded with the core action and accepted formats, then structured Args/Returns blocks that are easy to scan. No redundant restatement of the name or filler sentences.

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?

No output schema exists, and the description compensates by listing the return keys (dataset_id, rows, columns, column_names). Beyond grokking the three params, the only real gap is the ambiguity of conflicting sources and the unstated persistence side effect, which matter for an unannotated tool that creates server-side state.

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: all three parameters are explained, including the cross-parameter rule that file requires file_name and that an extensionless dataset_url also needs file_name. It does not state precedence when both file and dataset_url are supplied simultaneously, nor that all three are optional with null defaults.

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

Purpose5/5

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

States a specific verb and outcome (load a dataset into a pandas DataFrame) plus the two accepted sources (uploaded file bytes or a URL/path) and the supported extensions. It also names the sibling tools it is not (load_csv / load_excel / load_json / load_parquet), so an agent can route without opening any schema.

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

Usage Guidelines5/5

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

Gives an explicit when-not rule: for a file on the machine running the server, prefer the dedicated per-format loaders. The alternative path (this tool) is implicitly the one for already-uploaded bytes or remote URLs, which is stated directly.

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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