blackmount-nlp-mcp

clean_remove_numbers

Clean text by removing all numbers. Strips digits from input to leave only alphabetic and symbolic characters, preserving readability and structure.

Instructions

Remove all numbers from text.

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": {
      "text": "<text>"
    },
    "name": "clean_remove_numbers"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.2Apr 14, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states it removes numbers, but does not clarify edge cases (e.g., number formatting, Unicode digits) or mention whether the output is also a string. The presence of an output schema mitigates some transparency gaps.

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 extremely concise (one sentence), which is good for efficiency but it omits useful details. It could be improved by adding a brief usage note or example without significant bloat.

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

Completeness3/5

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

Given the tool's simplicity (single required parameter, output schema present), the description is minimally adequate. However, it lacks context about the cleaning pipeline, such as whether this is a preprocessing step or standalone, and does not differentiate from other number-related cleaning tools.

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?

The input schema has one parameter 'text' with no description (0% coverage). The description adds that numbers will be removed, but lacks specifics about the expected input format, encoding, or any constraints. It provides minimal added value beyond the schema.

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 removes all numbers from text, which is a specific verb+resource combination. It effectively distinguishes itself from sibling cleaning tools like clean_remove_emails or clean_remove_punctuation.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives, nor any prerequisites or limitations. The description does not mention that it might remove digits that are part of larger contexts like decimals or phone numbers.

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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