Recash1 MCP Server
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., "@Recash1 MCP Serversearch for laptops under $1000"
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.
Recash1 MCP Server
用于访问 Recash1 API 的 MCP 服务器。
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bach-recash1)🎉 点击 "安装 MCP" 按钮
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Related MCP server: DigiKey MCP Server
简介
这是一个 MCP 服务器,用于访问 Recash1 API。
PyPI 包名:
bach-recash1版本: 1.0.0
传输协议: stdio
安装
从 PyPI 安装:
pip install bach-recash1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-recash1 bach_recash1
# 或指定版本
uvx --from bach-recash1@latest bach_recash1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-recash1
# 运行(命令名使用下划线)
bach_recash1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-recash1": {
"command": "uvx",
"args": ["--from", "bach-recash1", "bach_recash1"],
"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-recash1": {
"command": "uvx",
"args": ["--from", "bach-recash1", "bach_recash1"],
"env": {
"API_KEY": "your_api_key_here"
}
}
}
}可用工具
此服务器提供以下工具:
search
This will filter what you want from the products on the data base
端点: GET /search
all_products
This will gives you all the products with codes on the data base
端点: GET /allproducts
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
2 toolsall_productsA
This will gives you all the products with codes on the data base
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states a read operation without mentioning side effects, authentication, or performance implications. The description is minimally transparent.
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 one sentence and reasonably concise, though it has a minor grammatical error ('will gives'). It front-loads the main purpose without unnecessary detail.
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?
With zero parameters and no output schema, the description is minimally adequate. However, it does not clarify whether 'all products' includes all databases or only those with codes, nor does it explain the relationship to the sibling tool. Some context is missing.
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 tool has no parameters, and the schema coverage is 100%. Per guidelines, a baseline of 4 applies since there are no parameters to describe. The description adds no parameter details because none exist.
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 retrieves all products with codes, using a verb ('gives') and resource ('all products'). The sibling tool 'search' provides implicit contrast for filtered results.
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?
No explicit guidance on when to use this tool over the sibling 'search', but the name 'all_products' implies listing all vs. searching. The description lacks explicit when-to-use or when-not-to-use information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchC
This will filter what you want from the products on the data base
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It only mentions 'filter' without disclosing behavior, return format, or constraints. Minimal disclosure.
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?
Single sentence, concise. However, it could be more informative and structured. Slightly above average.
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?
No output schema, no annotations, and description is minimal. For a filtering tool, it lacks details on how to filter, what is filtered, and return format. Incomplete.
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?
No parameters exist; schema coverage is 100% by default. Description adds no parameter info, so baseline 3 applies.
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?
Description states 'filter what you want from the products', which indicates a search/filter function but is vague. It does not distinguish from sibling tool 'all_products', leaving ambiguity about differences.
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?
No explicit guidance on when to use this tool versus alternatives. Usage is only implied by the vague description, with no exclusions or context.
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.
2 tool updates
v1.0.0- First observed
all_products - First observed
search
TDQS
The two tools have clearly distinct purposes: all_products retrieves all products, and search filters them. There is no overlap.
The naming mixes different styles: all_products uses snake_case with a noun, while search is a single verb. While both are recognizable, the lack of a consistent verb_noun pattern reduces clarity.
With only 2 tools, the server feels minimal for a product database. Typical CRUD operations are missing, making the tool set too thin for robust interaction.
The tools only support read operations (list all and search). There are no create, update, or delete tools, which are essential for complete product management. This is a significant gap.
Maintenance
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