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Aspose.Cells Cloud MCP Server

Aspose.Cells Cloud MCP Server

Official
by aspose-cells-cloud
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

List Supported Load Formats

list_supported_load_formats

Determine which file formats Aspose.Cells can read as input. Confirm support for CSV, JSON, XML, ODS, TXT before uploading or processing non-standard Excel files.

Instructions

Get Supported File Load/Import Formats

Use this tool to query the list of all source file formats that Aspose.Cells can read (load). Call this tool when a user wants to upload or process a non-standard Excel file (e.g., .csv, .json, .xml, .ods, .txt) and you need to verify if the system can parse it.

Returns:

  • Format Name: The name of the format (e.g., CSV, JSON, XML, ODS, XLSX, XLS, TXT, HTML).

  • Extension: The file suffix (e.g., .csv, .json).

  • Description: Characteristics of loading this format (e.g., 'Data only, no formatting', 'Requires specific parser', 'Preserves formulas').

Usage Guidelines:

  1. Before calling upload_file, if the user is uploading a file with an extension other than .xlsx or .xls, use this tool to confirm the format is recognizable.

  2. If a user asks 'Can you handle CSV files?', check this list and provide a confirmation.

  3. Distinguish between 'Load Formats' (Input) and 'Save Formats' (Output). Some formats may support reading but not writing (or vice versa).

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": "list_supported_load_formats"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv26.4.0

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of disclosing behavior. It explains the return fields (format name, extension, description), and importantly notes that some formats may support reading but not writing (or vice versa), which is a key behavioral nuance. It does not explicitly state that the tool is read-only and non-destructive, but the 'query' and 'returns' language implies this; a more explicit statement would push this to a 5.

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 description is well-structured with clear sections (purpose, returns, usage guidelines) and front-loads the core purpose. It uses Markdown headers and bullet points for scannability. While it is longer than minimal, every sentence earns its place by covering purpose, output, and usage discrimination, making it efficient for an agent to parse.

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 tool with no parameters, no output schema, and no annotations, the description is remarkably complete. It defines the exact output structure, explains when to invoke it (including a concrete example with upload_file), and clarifies the load/save distinction. An agent has everything needed to correctly call and interpret this tool without additional context.

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?

There are zero parameters in the input schema, so the baseline is 4 per the rubric. The description adds no parameter-specific semantics because there are none to document. However, it does describe the output structure, which is useful given the absence of an output schema, though this is outside the parameter-semantics dimension.

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?

The description clearly states the tool queries the list of all source file formats Aspose.Cells can load, using specific verbs ('query', 'list') and a resource ('supported load formats'). It explicitly differentiates from the sibling 'list_supported_save_formats' by emphasizing 'load' and noting the distinction in usage guidelines, leaving no ambiguity about its scope.

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?

The description provides explicit when-to-use instructions: before uploading non-standard files, when confirming a specific format like CSV, and it explicitly contrasts load vs. save formats. It even names the sibling tool list_supported_save_formats as the alternative for output formats, making the decision criteria clear and actionable.

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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