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aspose-cells-cloud

Aspose.Cells Cloud MCP Server

Official

Remove After Text

remove_after_text

Remove all characters after a specified delimiter in Excel cells using Aspose.Cells Cloud. Select worksheet, range, and case sensitivity to clean text data.

Instructions

Removes all characters after (to the right of) a specified delimiter text within each text cell in a worksheet range using Aspose.Cells Cloud API. Returns True if the operation succeeds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
_rangeNo
file_uuidYes
worksheetNo
case_sensitiveNo

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",
      -  "text"
      -]New value: +[
      +  "file_uuid",
      +  "text"
      +]
  2. First observedv26.4.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral burden. It discloses the core mutation, scope (each text cell in a worksheet range), direction, and success return. However, it does not say whether the file is modified in place, what happens when the delimiter is not found or appears multiple times, or whether the delimiter itself is retained (though 'after' implies it is).

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?

Two sentences, front-loaded with the action and scope, no filler. The API context and return value are stated economically.

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?

The description is far too thin for a 5-parameter mutation tool with no annotations and zero schema coverage. It lacks parameter explanations, side-effect disclosure, and guidance relative to sibling tools. The output schema covers the return, but that is the only well-covered aspect.

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

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%: none of the five parameters have descriptions. The description only clarifies that `text` is the delimiter; it leaves `_range`, `worksheet`, `case_sensitive`, and `file_uuid` unexplained. For a 0% coverage schema, this is insufficient.

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: removes characters after a delimiter text in each text cell of a worksheet range. It clarifies direction ('to the right of') and the spreadsheet context. It does not explicitly name sibling alternatives like remove_before_text, so it stops short of full differentiation.

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?

The description makes the operation clear enough that an agent can infer when to use it (when trimming trailing content after a delimiter in spreadsheet cells), but it gives no explicit when-to-use vs alternatives, no exclusions, and no mention of remove_before_text for the opposite case.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.