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Humanizer APIs MCP Server

Humanizer APIs MCP Server

by BACH-AI-Tools
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Python
Remote

Humanizer Apis MCP Server

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用于访问 Humanizer Apis API 的 MCP 服务器。

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Related MCP server: swagger-mcp-server

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:

pip install bach-humanizer_apis

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-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）

python server.py

方式 3: 安装后作为命令运行

# 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:

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-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:

{
  "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 文件。

开发

此服务器由 API-to-MCP 工具生成。

版本: 1.0.0

Available Tools

3 tools
basic_modelC

Basic model (lightweight, useful)

Inputs: no named parameters.

easy_use_humanizerD

Easy use

Inputs: no named parameters.

multi_languagesC

Best model for multi languages

Inputs: no named parameters.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    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
    ResponsivenessOutdated data

    Resources

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