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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Load Excel

load_excel

Load an Excel file (.xls or .xlsx) from disk into a new dataset, optionally selecting a sheet, and return dataset ID, row and column counts, and column names.

Instructions

Load an Excel file (.xls or .xlsx) from disk into a new dataset. Args: file_path (str): Full path to the Excel file on the machine running the server. sheet_name (str, optional): Sheet to read. Default is the first sheet. 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_excel"
  }
}

Input Schema

NameRequiredDescriptionDefault
file_pathYes
sheet_nameNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
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 that the path is resolved on the machine running the server, that a new dataset is created, that sheet_name defaults to the first sheet, and what the response contains. It stops short of covering failure modes (missing file, bad sheet name) or whether an existing dataset is affected.

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?

Front-loaded purpose sentence followed by Args and Returns blocks, with no wasted words. The docstring-style layout is slightly verbose for two parameters but every line carries information.

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

Completeness5/5

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

For a simple two-parameter read tool with no annotations and no output schema, the definition supplies purpose, both parameter meanings, default behavior, and the return structure. An agent has everything needed 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 coverage is 0%, so the description must compensate, and it does: both parameters are documented, with 'full path... on the machine running the server' adding critical meaning the bare string type does not convey, and the default-first-sheet behavior for the optional sheet_name. It could go further by noting path separators or platform conventions.

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?

States a specific verb and resource ('Load an Excel file (.xls or .xlsx) from disk into a new dataset'), including the accepted extensions and the outcome (a new dataset). It does not explicitly name a sibling such as load_csv, but the format restriction alone makes the boundary obvious to an agent choosing among the load_* tools.

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 the file-format constraint; there is no explicit 'use this when...' or exclusion statement, and no mention of prerequisites such as the file existing on the server filesystem. Adequate but with clear gaps in routing guidance.

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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