Similar Words MCP Server
Provides access to the Similar Words API via the RapidAPI platform, enabling tools to find similar words and related linguistic information.
Click on "Deploy 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., "@Similar Words MCP Serverfind words similar to 'innovation'"
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.
Similar Words MCP Server
用于访问 Similar Words API 的 MCP 服务器。
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Related MCP server: LangSearch MCP Server
简介
这是一个 MCP 服务器,用于访问 Similar Words API。
PyPI 包名:
bach-similar_words版本: 1.0.0
传输协议: stdio
安装
从 PyPI 安装:
pip install bach-similar_words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-similar_words bach_similar_words
# 或指定版本
uvx --from bach-similar_words@latest bach_similar_words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-similar_words
# 运行(命令名使用下划线)
bach_similar_words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-similar_words": {
"command": "uvx",
"args": ["--from", "bach-similar_words", "bach_similar_words"],
"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-similar_words": {
"command": "uvx",
"args": ["--from", "bach-similar_words", "bach_similar_words"],
"env": {
"API_KEY": "your_api_key_here"
}
}
}
}可用工具
此服务器提供以下工具:
api_endpoint
请根据 RapidAPI 页面手动添加端点信息
端点: GET /endpoint
参数:
param(string): 参数
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 toolapi_endpointD
请根据 RapidAPI 页面手动添加端点信息
| Name | Required | Description | Default |
|---|---|---|---|
| param | No | 参数 |
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 of behavioral disclosure. It does not mention any side effects, permissions, persistence, reversibility, or return behavior. For a tool that 'adds' information, there is no indication of what changes occur or what the output looks like.
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 concise (one sentence) and front-loaded, but it is under-specified. It earns its place only slightly by mentioning RapidAPI as a source, yet the brevity does not compensate for the lack of meaningful content.
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?
For a tool with one optional parameter and no output schema, the description is extremely incomplete. It fails to explain the tool's purpose, how the parameter relates to the endpoint information, what the expected behavior is, or what the user should observe after invocation.
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 schema has one optional parameter 'param' with a description '参数' (parameter), which is tautological and provides no real semantics. The tool description adds nothing about how to use the parameter or what values it expects. Although schema coverage is 100%, the content is useless, and the description does not compensate.
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 'Please manually add endpoint information based on the RapidAPI page' uses a specific verb ('add') but the resource ('endpoint information') is vague and the phrase reads more like an instruction to a human than a functional tool description. It does not clearly define what the tool actually does or what constitutes endpoint information, making it barely rise above a tautology.
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?
There is no guidance on when to use this tool versus alternatives. The phrase 'based on the RapidAPI page' implies a context but does not explicitly state usage scenarios, prerequisites, or exclusions. No alternatives are mentioned.
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.
1 tool update
v1.0.0- First observed
api_endpoint
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'api_endpoint' stands alone with a distinct purpose of adding endpoint information from RapidAPI pages.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'api_endpoint' uses a clear and descriptive snake_case format, which sets a good precedent if more tools were added.
A single tool is too few for most server purposes, as it severely limits functionality and suggests the server is underdeveloped. For a server named 'Similar Words MCP Server', one might expect multiple tools for tasks like finding synonyms, antonyms, or word comparisons, making this count inappropriate.
The tool set is severely incomplete for the inferred domain of word similarity. With only one tool for adding endpoint information, there are no tools for core operations like querying similar words, comparing terms, or managing word lists, leaving significant gaps that will cause agent failures.
Maintenance
Related MCP Connectors
The Needle MCP server enables semantic search on documents stored in files like PDFs, DOCX, and XLSX by connecting AI applications to external data sources. It provides capabilities to create and manage document collections, perform natural language searches on stored content, and retrieve relevant information without requiring exact keyword matches.
Words MCP — wraps Datamuse API (free, no auth required)
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