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BACH-AI-Tools

Sentiment By Api Ninjas MCP Server

by BACH-AI-Tools
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Python
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Sentiment By Api Ninjas MCP Server

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用于访问 Sentiment By Api Ninjas API 的 MCP 服务器。

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Related MCP server: Nutrition By Api Ninjas MCP Server

简介

这是一个 MCP 服务器，用于访问 Sentiment By Api Ninjas API。

  • PyPI 包名: bach-sentiment_by_api_ninjas

  • 版本: 1.0.0

  • 传输协议: stdio

安装

从 PyPI 安装:

pip install bach-sentiment_by_api_ninjas

从源码安装:

pip install -e .

运行

方式 1: 使用 uvx（推荐，无需安装）

# 运行（uvx 会自动安装并运行）
uvx --from bach-sentiment_by_api_ninjas bach_sentiment_by_api_ninjas

# 或指定版本
uvx --from bach-sentiment_by_api_ninjas@latest bach_sentiment_by_api_ninjas

方式 2: 直接运行（开发模式）

python server.py

方式 3: 安装后作为命令运行

# 安装
pip install bach-sentiment_by_api_ninjas

# 运行（命令名使用下划线）
bach_sentiment_by_api_ninjas

配置

API 认证

此 API 需要认证。请设置环境变量:

export API_KEY="your_api_key_here"

环境变量

变量名

说明

必需

API_KEY

API 密钥

是

PORT

不适用

否

HOST

不适用

否

在 Cursor 中使用

编辑 Cursor MCP 配置文件 ~/.cursor/mcp.json:

{
  "mcpServers": {
    "bach-sentiment_by_api_ninjas": {
      "command": "uvx",
      "args": ["--from", "bach-sentiment_by_api_ninjas", "bach_sentiment_by_api_ninjas"],
      "env": {
        "API_KEY": "your_api_key_here"
      }
    }
  }
}

在 Claude Desktop 中使用

编辑 Claude Desktop 配置文件 claude_desktop_config.json:

{
  "mcpServers": {
    "bach-sentiment_by_api_ninjas": {
      "command": "uvx",
      "args": ["--from", "bach-sentiment_by_api_ninjas", "bach_sentiment_by_api_ninjas"],
      "env": {
        "API_KEY": "your_api_key_here"
      }
    }
  }
}

可用工具

此服务器提供以下工具:

v1sentiment

Returns sentiment analysis score and overall sentiment for a given block of text.

端点: GET /v1/sentiment

参数:

  • text (string) 必需: query text for sentiment analysis. Maximum 2000 characters.

技术栈

  • 传输协议: stdio

  • HTTP 客户端: httpx

许可证

MIT License - 详见 LICENSE 文件。

开发

此服务器由 API-to-MCP 工具生成。

版本: 1.0.0

Available Tools

1 tool
v1sentimentB

Returns sentiment analysis score and overall sentiment for a given block of text.

ParametersJSON Schema
NameRequiredDescriptionDefault
textYesquery text for sentiment analysis. Maximum 2000 characters.

TDQS

B3.3/5.0
Behavior2/5

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

Without annotations, the description carries full burden. It only states that the tool returns a score and overall sentiment, but fails to describe the score range, output format, or any behavioral traits like limits or side effects beyond the input schema's max length.

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 wasted words. It is front-loaded and to the point, achieving maximum conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple with one parameter and no output schema. The description covers the basic purpose but lacks details about the output structure (e.g., score range, sentiment categories). It is complete enough for a minimal viable description but has gaps.

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?

Schema description coverage is 100% for the single parameter 'text', which includes a clear description. The tool description adds minimal extra context, essentially restating the schema. Baseline 3 is appropriate as the schema already provides adequate info.

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 uses a specific verb 'Returns' and clearly names the resource: sentiment analysis score and overall sentiment for text. It precisely states what the tool does without ambiguity.

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 vs. alternatives or when not to use it. There is no mention of prerequisites, context, or exclusions.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev1.0.0
    • First observedv1sentiment
Install Server
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TDQS

A3.5/5.0
Disambiguation5/5

Only one tool exists, so there is no risk of confusion with other tools.

Naming Consistency5/5

With a single tool, naming consistency is inherently perfect.

Tool Count3/5

One tool for a sentiment analysis server is minimal but acceptable if the scope is narrow; however, it feels thin for typical use.

Completeness3/5

The tool covers the core function of sentiment analysis, but lacks supplementary features like batch processing or multiple models, leaving some gaps.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

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  • Sentiment402 MCP Adapter

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    Provides access to Sentiment402 market sentiment data for global markets, crypto, TradFi, and specific assets, with built-in support for x402 micropayment protocol when API responses require payment.
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  • Nutrition By Api Ninjas MCP

    BACH-AI-Tools
    A
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    Provides access to the Nutrition By Api Ninjas API to extract detailed nutritional information from natural language food queries. It enables users to retrieve data such as calories, vitamins, and macros from text descriptions of meals.
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  • API Ninjas MCP

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