Allows AI assistants to retrieve API endpoint definitions and specifications from Apifox projects, including request methods, headers, parameters, and response schemas for improved code generation and development efficiency.
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., "@Apifox MCP Server get details for the user login endpoint in project 12345"
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.
一个基于 Stdio 实现的模型上下文协议 (Model Context Protocol) 服务器，旨在无缝桥接 AI 助手与 Apifox 平台的 API 数据。
允许 AI 助手通过标准化的协议直接、实时地获取和理解您在 Apifox 项目中定义的 API 接口详情，从而显著提升开发效率和代码生成质量。
get_api_endpoint_info - 获取 Apifox 的接口定义信息
请求参数
projectId: Apifox 项目的唯一标识符。
endpointId: 目标 API 端点的唯一标识符。
返回数据
一个结构化的 JSON 对象，包含完整的 API 规约 (Specification)，例如：
HTTP 请求方法 (Method)
求头 (Request Headers)
路径、查询及请求体参数 (Parameters: Path, Query, Body)
响应体结构 (Response Body Schema)
响应头 (Response Headers)
Related MCP server: Apidog MCP Server
要在 Cursor 中使用此服务器，请添加以下服务器配置:
{
"mcpServers": {
"Apifox-MCP": {
"command": "npx",
"args": ["@juzi965/apifox-mcp-server"],
"env": {
"APIFOX_AUTH": "..."
}
}
}
}该服务器需要以下环境变量:
APIFOX_AUTH: Apifox 的认证令牌
复制协作链接到 Cursor 中，让它帮你完成
入参的 TS 类型定义
出参的 TS 类型定义
接口调用方法的定义
由于 MCP 服务器通过标准输入输出(stdio)通信，调试可能比较困难。我们推荐使用MCP Inspector，可通过以下命令启动:
npm run inspectorInspector 将提供一个 URL，可在浏览器中访问调试工具。
获取apifox的接口定义信息，数据符合OpenAPI 3.1规范。遇到例如：https://app.apifox.com/link/project/{projectId}/apis/api-{endpointId}的链接，请解析出projectId和endpointId，并调用本工具获取接口定义信息。
| Name | Required | Description | Default |
|---|---|---|---|
| projectId | Yes | Apifox的项目ID | |
| endpointId | Yes | 接口Endpoint的ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the output conforms to OpenAPI 3.1 specification, implying a read operation. However, it does not address potential errors, auth requirements, or whether the tool is read-only, which is a minor gap.
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 at two sentences. The first sentence states the main purpose, and the second provides a practical example. No redundant information.
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 tool's simplicity (2 parameters, no output schema), the description covers the core functionality and output format (OpenAPI 3.1). It does not mention error conditions or response handling, but the context from schema coverage compensates.
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?
Schema coverage is 100% with descriptions already provided. The description adds significant value by explaining how to derive the parameters from a URL, which is not inferable from the schema alone.
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 API endpoint definitions from apifox conforming to OpenAPI 3.1. It specifies the exact data format and provides a concrete usage scenario with URL parsing, making the purpose unambiguous.
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 explicitly instructs when to use the tool: when encountering a specific URL pattern. It provides step-by-step guidance to extract parameters, but does not explicitly mention when not to use it or list alternatives, though no siblings exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0get_api_endpoint_info2 fields changedInput schema / $schemaAdded value: +"http://json-schema.org/draft-07/schema#"Input schema / additionalPropertiesAdded value: +falseget_api_endpoint_infoOnly one tool exists, so there is no risk of confusion with other tools. The tool's purpose is clearly defined and distinct by default.
With a single tool, naming consistency is inherently perfect. The name 'get_api_endpoint_info' follows a clear verb_noun pattern and accurately describes its function.
The server has only one tool, which is on the thin side for a typical API integration. While the tool is not trivial, a more comprehensive set (e.g., listing projects or endpoints) would be expected for this domain.
The tool provides a single operation without supporting tools for discovery or listing of projects/endpoints. Agents must already know the projectId and endpointId, which creates a significant gap for autonomous workflows.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
AI-callable tools for API mocking, testing, monitoring, security, and automation.
Discover, compare, and monitor 1,400+ APIs directly from your AI coding agent.
Turn any task into the right API calls: discover, evaluate, and integrate public APIs.
Enable AI assistants to interact seamlessly with Feeef e-commerce stores, products, and orders usi…
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/Juzisuan965/apifox-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
