blackmount-nlp-mcp

generate_char_ngrams

Generate character-level n-grams from text. Specify the n value (default 3) to produce fixed-length substrings for analysis tasks like classification or spelling correction.

Instructions

Generate character-level n-grams 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": "generate_char_ngrams"
  }
}

Input Schema

Name	Required	Description	Default
`text`	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

B3.2/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It fails to disclose important behaviors such as how whitespace is handled, minimum/maximum n values, overlapping windows, or the format of the output. The existence of an output schema is not evident from the description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise at one sentence, front-loading the key information. While this is efficient, it sacrifices necessary detail for completeness. Still, it earns a high score for structure.

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?

Given the tool's simplicity, the description might seem adequate, but it omits critical context like the range of 'n', handling of edge cases (empty string), and the nature of the generated n-grams. The output schema is not described, leaving the agent guessing about return 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?

The description adds minimal meaning beyond the schema. It mentions 'text' and 'character-level n-grams' but does not explain the 'n' parameter (e.g., must be positive integer) or constraints. Schema description coverage is 0%, so the description should compensate but does not.

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 action (generate) and resource (character-level n-grams from text). It distinguishes itself from the sibling tool 'generate_ngrams' by specifying 'character-level', making the purpose unambiguous.

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 character-level n-gram generation but does not provide explicit guidance on when to use this tool versus the word-level 'generate_ngrams' or other related tools. No scenarios or exclusions are mentioned.

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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