Random Word By Api Ninjas
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Random Word By Api Ninjasgive me a random adjective for my poem"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Random Word By Api Ninjas MCP Server
用于访问 Random Word By Api Ninjas API 的 MCP 服务器。
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🌐 访问 EMCP 平台
📝 注册并登录账号
🎯 进入 MCP 广场,浏览所有可用的 MCP 服务器
🔍 搜索或找到本服务器(
bach-random_word_by_api_ninjas)🎉 点击 "安装 MCP" 按钮
✅ 完成!即可在您的应用中使用
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Related MCP server: Thesaurus By Api Ninjas
简介
这是一个 MCP 服务器,用于访问 Random Word By Api Ninjas API。
PyPI 包名:
bach-random_word_by_api_ninjas版本: 1.0.0
传输协议: stdio
安装
从 PyPI 安装:
pip install bach-random_word_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-random_word_by_api_ninjas bach_random_word_by_api_ninjas
# 或指定版本
uvx --from bach-random_word_by_api_ninjas@latest bach_random_word_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-random_word_by_api_ninjas
# 运行(命令名使用下划线)
bach_random_word_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 密钥 | 是 |
| 不适用 | 否 |
| 不适用 | 否 |
在 Cursor 中使用
编辑 Cursor MCP 配置文件 ~/.cursor/mcp.json:
{
"mcpServers": {
"bach-random_word_by_api_ninjas": {
"command": "uvx",
"args": ["--from", "bach-random_word_by_api_ninjas", "bach_random_word_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-random_word_by_api_ninjas": {
"command": "uvx",
"args": ["--from", "bach-random_word_by_api_ninjas", "bach_random_word_by_api_ninjas"],
"env": {
"API_KEY": "your_api_key_here"
}
}
}
}可用工具
此服务器提供以下工具:
v1randomword
API Ninjas Random Word API endpoint. Returns a random word.
端点: GET /v1/randomword
参数:
type(string): Type of word. Possible values are: noun, verb, adjective, adverb.
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 toolv1randomwordC
API Ninjas Random Word API endpoint. Returns a random word.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Type of word. Possible values are: noun, verb, adjective, adverb. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool returns a random word but does not disclose behavioral traits such as rate limits, authentication needs, error handling, or response format. This leaves significant gaps in understanding how the tool behaves in practice.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, consisting of only two sentences that directly state the tool's purpose and source. Every sentence earns its place with zero waste, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It does not explain return values, error conditions, or other contextual details needed for effective use. While the tool is simple, the description fails to provide sufficient completeness for a tool with no structured support.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, documenting the optional 'type' parameter with possible values. The description does not add any parameter semantics beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without compensating value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Returns a random word' with the specific source 'API Ninjas Random Word API endpoint.' It uses a specific verb ('Returns') and resource ('random word'), but since there are no sibling tools, it cannot differentiate from them, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. It mentions the API endpoint but does not specify use cases, prerequisites, or exclusions. Without sibling tools, there is no explicit comparison, but general usage context is still missing.
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 tool update
- First observed
v1randomword
TDQS
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as returning a random word, making it impossible to confuse with other tools.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'v1randomword' follows a single pattern with no deviations to compare against.
A single tool is too few for most practical server purposes, as it limits functionality and scope. While it matches the simple API of returning a random word, it feels thin and underdeveloped for broader agent use.
For the narrow domain of fetching a random word, the tool surface is complete. There are no gaps since the only required operation is provided, and no additional actions like filtering or updating are needed for this purpose.
Maintenance
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