blackmount-nlp-mcp

generate_ngrams

Generate n-grams from a list of tokens for text analysis. Returns a list of n-gram lists, useful for language modeling and feature extraction.

Instructions

Generate n-grams from a list of tokens. Returns list of n-gram lists.

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": {
      "tokens": "<tokens>"
    },
    "name": "generate_ngrams"
  }
}

Input Schema

Name	Required	Description	Default
`tokens`	Yes
`n`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.2Apr 14, 2026

TDQS

A3.5/5.0

Behavior3/5

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

No annotations are provided, so the description must carry the burden. It states the output format but does not mention performance, side effects (none expected), or assumptions (e.g., tokens are pre-tokenized). It is adequate but minimal.

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 sentences, front-loaded with the purpose, and contains no redundant 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 the low complexity, clear schema, and presence of output schema, the description is sufficient. It lacks usage guidance but covers the essential purpose and output format.

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% as the description does not elaborate on parameters. While parameter names are self-explanatory, the description adds no extra meaning or constraints beyond the schema. For a simple tool, this is a minor gap.

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's action (generate n-grams) and input (list of tokens) and output (list of n-gram lists). It distinguishes from sibling tool 'generate_char_ngrams' which implies character-level n-grams.

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 vs alternatives like 'generate_char_ngrams' or other text processing tools. The description does not provide context for typical use cases or prerequisites.

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

Deploy Server