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blackmount-nlp-mcp

blackmount-nlp-mcp

by BlackMount-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_aspect_sentiment

Extract sentiment scores for specified aspects in text by detecting related sentences and computing average sentiment.

Instructions

Sentiment around specific topics/aspects. Finds sentences mentioning each aspect and averages their sentiment.

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>",
      "aspects": "<aspects>"
    },
    "name": "get_aspect_sentiment"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes
aspectsYes

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?

The description discloses that the tool finds sentences and averages sentiment, but it does not cover edge cases (e.g., aspect not found) or return format. With no annotations, additional behavioral context would be helpful.

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?

Two sentences, no redundancy. Every piece of text adds value: the first sentence states the purpose, the second explains the mechanism.

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?

The description does not explain the output format (e.g., sentiment scale, data structure) or behavior when aspects are not found. Without an output schema, these details are necessary for completeness.

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?

Despite 0% schema description coverage, the description adds meaning by explaining that 'text' is the input and 'aspects' is a list of topics, and it describes the process of finding sentences and averaging sentiment. This compensates well for the lack of schema descriptions.

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: finding sentences mentioning each aspect and averaging their sentiment. It distinguishes from sibling tools like get_sentence_sentiments and get_sentiment_score by focusing on aspect-level sentiment.

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 aspect-based sentiment analysis but lacks explicit guidance on when to use vs. alternatives or when not to use. 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.

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