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blackmount-nlp-mcp

blackmount-nlp-mcp

by BlackMount-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_sentence_sentiments

Analyze each sentence in a text to retrieve sentiment scores and labels, offering granular understanding of emotional tone across sentences.

Instructions

Per-sentence sentiment breakdown. Returns list of {sentence, score, label}.

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": "get_sentence_sentiments"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A3.8/5.0
Behavior3/5

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

No annotations provided. Description discloses return format (list of {sentence, score, label}) but lacks details on score range, label meaning, input constraints, or side effects.

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?

Extremely concise: one sentence and a brief data structure. Every word adds value, no redundancy.

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?

For a simple 1-parameter tool with an output schema, the description covers the essential functionality. Could specify score/label semantics but sufficiently complete for agent use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Single parameter 'text' is self-explanatory, but with 0% schema description coverage, the tool description adds minimal extra meaning 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?

Clearly states it performs per-sentence sentiment breakdown and returns a list with sentence, score, label. Distinct from siblings like get_sentiment_label or get_sentiment_score which operate on whole text.

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?

Implied usage for sentence-level sentiment analysis, but no explicit when-to-use or when-not-to-use guidance nor mention of alternative tools (e.g., for overall sentiment) in the description.

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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