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aspose-cells-cloud

Aspose.Cells Cloud MCP Server

Official
by aspose-cells-cloud
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Remove Punctuation Marks

remove_punctuation_marks

Cleans text cells in Excel files by deleting punctuation marks while keeping letters, numbers, symbols, and spaces; prepare data for NLP, word counting, or text mining by removing punctuation noise.

Instructions

Removes punctuation marks from text cells while preserving alphanumeric characters and other symbols. This function uses Aspose.Cells Cloud API to process an Excel file and delete punctuation marks from text within cells. It removes sentence-ending punctuation (periods, question marks, exclamation points), internal punctuation (commas, semicolons, colons), quotation marks, apostrophes, parentheses, brackets, and other punctuation. However, it preserves letters (A-Z, a-z), numbers (0-9), spaces, and other symbol characters like currency symbols ($, €, £), mathematical operators (+, -, =, *, /), and special symbols (@, #, %, &, etc.). This is useful for text preprocessing in NLP, removing sentence boundaries for analysis, cleaning text for word counting, or preparing data for text mining where punctuation is considered noise.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
_rangeNo
file_uuidYes
worksheetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv26.4.1
    • removedInput schema / properties / file_token
      Removed value: -{
-  "type": "string"
-}
    • addedInput schema / properties / file_uuid
      Added value: +{
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "file_token"
-]New value: +[
+  "file_uuid"
+]
  2. First observedv26.4.0

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it explains what characters are removed and preserved, it does not mention side effects such as whether the original file is modified or a new file is returned, permissions required, rate limits, or idempotency. The reference to Aspose.Cells Cloud API adds some context but does not cover the safety profile.

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

Conciseness3/5

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

The description is front-loaded with the main purpose, but it is verbose and contains redundancy. The second sentence repeats the removal action and adds only the API name. The long lists of punctuation and preserved symbols are useful but could be condensed. It does not waste words overall, but it is longer than necessary.

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

Completeness2/5

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

Given the three parameters, zero parameter documentation, and no annotations, the description is incomplete. It explains the functional transformation well but omits parameter semantics and side effects. The presence of an output schema reduces the need to describe return values, but the tool still lacks enough context for safe invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

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 for the three undocumented parameters (file_uuid, _range, worksheet). It never explains the meaning or format of these parameters, how ranges are specified, or what worksheet defaults to. Without this information, an agent cannot reliably fill in the optional parameters.

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 identifies a specific verb and resource: removes punctuation marks from text cells. It distinguishes itself from sibling tools by explicitly detailing what is preserved (letters, numbers, spaces, and other symbols like currency signs and mathematical operators), which separates it from remove_symbols or remove_custom_characters. An agent can understand exactly what kind of transformation this tool performs.

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

Usage Guidelines4/5

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

The description gives specific use cases for when this tool is appropriate (text preprocessing in NLP, removing sentence boundaries, cleaning text for word counting, text mining). It does not explicitly mention alternatives or when not to use it, but the context is clear enough that an agent can select it for punctuation-stripping tasks.

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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