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blackmount-nlp-mcp

blackmount-nlp-mcp

by BlackMount-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

sentence_tokenize

Split text into sentences by accurately handling abbreviations like Mr. and Dr. and complex boundary conditions, ensuring reliable sentence segmentation.

Instructions

Split text into sentences. Handles abbreviations (Mr., Dr., etc.) and tricky boundaries.

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": "sentence_tokenize"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses handling of abbreviations and tricky boundaries, indicating awareness of common edge cases. However, it does not mention behavior for empty input, whitespace-only text, or performance.

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 two concise sentences, front-loaded with purpose, and no wasted words. Every sentence provides essential information.

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

Completeness4/5

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

Given an output schema exists (not shown), explaining return values is not required. The description covers main purpose and key behavioral aspect (abbreviations). It could mention handling of newlines or multiple punctuation, but for a simple tool it is sufficient.

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 description coverage is 0%, so the description must compensate. It only refers to 'text' via 'Split text', but does not add details about encoding, limits, or format. The description adds minimal value beyond the parameter name.

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 'Split text into sentences' which is a direct verb+resource. It distinguishes from sibling tools like word_tokenize (word-level) and count_sentences (count only) by implying output is segmented sentences. The mention of handling abbreviations and tricky boundaries adds specificity.

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 implies usage for sentence segmentation but does not provide explicit guidelines on when to use this tool versus alternatives like regex-based splitting or other tokenizers. No exclusions or prerequisites are mentioned.

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

Deploy Server

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