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blackmount-nlp-mcp

blackmount-nlp-mcp

by BlackMount-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

porter_stem

Reduce English words to their base stem using the Porter stemming algorithm. Input a word and get its stem, e.g., 'running' becomes 'run'.

Instructions

Porter stemmer from scratch. Reduce word to its stem (e.g., 'running' -> 'run').

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": {
      "word": "<word>"
    },
    "name": "porter_stem"
  }
}

Input Schema

NameRequiredDescriptionDefault
wordYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A3.7/5.0
Behavior2/5

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

With no annotations, the description should carry the full burden. It names the algorithm ('Porter stemmer from scratch') and provides an example, but fails to mention edge cases (empty string, non-English), case sensitivity, or output format.

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 exceptionally concise: two sentences, no fluff. Front-loaded with the algorithm name and clear purpose, followed by an illustrative example.

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?

For a simple tool with one parameter and no annotations, the description is adequate but omits details about return value format (though output schema exists but not displayed), language support, and handling of non-alphabetic characters.

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% (no description for 'word'). The description adds the meaning that 'word' is the input to be reduced to its stem, with an example illustrating the transformation. This compensates for the lack of schema description.

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 is a Porter stemmer that reduces words to their stems, with a concrete example ('running' -> 'run'). It distinguishes itself from sibling tools like text cleaners and readability metrics.

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?

No guidance on when to use stemming vs alternatives. While there are no other stemmers among siblings, the description does not explain contexts (e.g., before keyword extraction) or when not to use (e.g., for proper nouns).

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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