ZapifyAPI MCP Server

analyze_sentiment

Determine the sentiment of any text by classifying it as positive, negative, or neutral. Gain quick insights into customer opinions and feedback.

Instructions

Detect positive/negative/neutral sentiment in 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": "analyze_sentiment"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 3, 2026

TDQS

B3.1/5.0

Behavior2/5

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

With no annotations, the description holds the full burden of behavioral disclosure. It only states the core action and does not describe the output format, supported languages, input length limits, or whether it returns a label, score, or both. This leaves significant ambiguity about the tool's behavior.

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 sentence with no redundant words. It is appropriately concise for a simple tool and includes the essential information about purpose.

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 lack of output schema and annotations, the description should explain what the tool returns. It does not, so the agent cannot know the structure of the result. This is a clear gap that leaves the description incomplete.

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 schema contains a single 'text' parameter with no description and 0% schema coverage. The description references 'text' but provides no additional semantic detail such as examples, encoding, length, or domain. It does not compensate for the lack of parameter documentation.

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 function: detecting positive/negative/neutral sentiment in text. It uses a specific verb and resource, and while it doesn't explicitly differentiate from siblings, there are no other sentiment analysis tools among the siblings, so the purpose is unambiguous.

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?

The description provides no guidance on when to use this tool versus alternatives. There is no mention of exclusions, prerequisites, or contextual examples. It simply states what the tool does without addressing usage scenarios.

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