blackmount-nlp-mcp

get_sentiment_score

Analyze text sentiment with a compound score from -1 (negative) to 1 (positive) using a VADER-style lexicon of 2000+ words.

Instructions

Compound sentiment score from -1 (negative) to 1 (positive). VADER-style with built-in 2000+ word lexicon.

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_sentiment_score"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes

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?

With no annotations, the description carries full burden. It discloses the output range and method (VADER, lexicon), which is moderate transparency. However, it omits details like language assumptions, computational cost, or whether the tool is deterministic.

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 a single, front-loaded sentence with no unnecessary words. It efficiently communicates the core purpose and method.

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 tool's simplicity (one parameter, clear output), the description is fairly complete. The existence of an output schema reduces the need to detail return structure, and the description summarizes the range. Could mention applicable languages or caveats but is sufficient for a straightforward tool.

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 should add meaning to the 'text' parameter beyond being a string. It does not mention expected format, length limits, encoding, or preprocessing steps, thus offering no extra value.

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 returns a compound sentiment score from -1 to 1, specifies the VADER-style method and built-in lexicon, and distinguishes itself from siblings like get_sentiment_label or get_aspect_sentiment by indicating it gives an overall polarity score.

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 is provided on when to use this tool versus alternatives such as get_sentiment_label or get_aspect_sentiment. The description does not mention any prerequisites, exclusions, or context for ideal use.

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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