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BACH-AI-Tools

Humanizer APIs MCP Server

README.md
# Humanizer Apis MCP Server

[English](./README_EN.md) | 简体中文 | [繁體中文](./README_ZH-TW.md)

用于访问 Humanizer Apis API 的 MCP 服务器。

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---

## 简介

这是一个 MCP 服务器,用于访问 Humanizer Apis API。

- **PyPI 包名**: `bach-humanizer_apis`
- **版本**: 1.0.0
- **传输协议**: stdio


## 安装

### 从 PyPI 安装:

```bash
pip install bach-humanizer_apis
```

### 从源码安装:

```bash
pip install -e .
```

## 运行

### 方式 1: 使用 uvx(推荐,无需安装)

```bash
# 运行(uvx 会自动安装并运行)
uvx --from bach-humanizer_apis bach_humanizer_apis

# 或指定版本
uvx --from bach-humanizer_apis@latest bach_humanizer_apis
```

### 方式 2: 直接运行(开发模式)

```bash
python server.py
```

### 方式 3: 安装后作为命令运行

```bash
# 安装
pip install bach-humanizer_apis

# 运行(命令名使用下划线)
bach_humanizer_apis
```

## 配置

### API 认证

此 API 需要认证。请设置环境变量:

```bash
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`:


```json
{
  "mcpServers": {
    "bach-humanizer_apis": {
      "command": "uvx",
      "args": ["--from", "bach-humanizer_apis", "bach_humanizer_apis"],
      "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`:

```json
{
  "mcpServers": {
    "bach-humanizer_apis": {
      "command": "uvx",
      "args": ["--from", "bach-humanizer_apis", "bach_humanizer_apis"],
      "env": {
        "API_KEY": "your_api_key_here"
      }
    }
  }
}
```


## 可用工具

此服务器提供以下工具:


### `easy_use_humanizer`

Easy use

**端点**: `POST /humanizer`



---


### `basic_model`

Basic model (lightweight, useful)

**端点**: `POST /humanizer/basic`



---


### `multi_languages`

Best model for multi languages

**端点**: `POST /humanizer/language`



---



## 技术栈

- **传输协议**: stdio
- **HTTP 客户端**: httpx


## 许可证

MIT License - 详见 [LICENSE](./LICENSE) 文件。

## 开发

此服务器由 [API-to-MCP](https://github.com/BACH-AI-Tools/api-to-mcp) 工具生成。

版本: 1.0.0

TDQS

C2/5.0

Scored across 3 tools

Disambiguation2/5

The tools have overlapping and unclear purposes: 'basic_model' and 'easy_use_humanizer' both seem to target general humanization tasks without clear differentiation, and 'multi_languages' might overlap with the others in multilingual contexts. The vague descriptions ('lightweight, useful', 'Easy use') provide little help in distinguishing them, leading to potential misselection.

Naming Consistency2/5

Naming is inconsistent with mixed conventions: 'basic_model' uses snake_case, 'easy_use_humanizer' mixes snake_case with a compound name, and 'multi_languages' uses snake_case but lacks a clear verb pattern. There is no predictable naming scheme across the set, making it harder for agents to infer tool purposes from names alone.

Tool Count3/5

With 3 tools, the count is borderline for a server named 'Humanizer APIs MCP Server', which suggests a broader scope. This feels thin as it may not cover essential humanization operations (e.g., text formatting, localization, or specific transformations), but it's not severely mismatched like having only one tool.

Completeness2/5

Inferred domain is text humanization or localization, but the tool set has significant gaps: there are no clear CRUD operations (e.g., create, update, delete humanized content), no specific input/output handling tools, and the vague tools don't cover a complete workflow. This will likely cause agent failures due to missing core functionalities.

Maintenance

ActivityInactive
ResponsivenessNo issues