一个 MCP 服务器，用于根据 Postman 集合和请求生成 AI 代理工具。该服务器与 Postman API 集成，可将 API 端点转换为可与各种 AI 框架一起使用的类型安全代码。
模型上下文协议 (MCP) 是一种新的标准化协议，用于管理大型语言模型 (LLM) 与外部系统之间的上下文。在此存储库中，我们提供了Postman 工具生成 API的安装程序和 MCP 服务器。
这使您可以使用Claude Desktop或任何 MCP 客户端（如Cline ）使用自然语言在您的 Postman 帐户上完成操作，例如：
Create an AI tool for: collectionID: 12345-abcde requestID: 67890-fghij typescript openai
从 Postman 集合生成 TypeScript/JavaScript 代码
支持多种AI框架（OpenAI、Mistral、Gemini、Anthropic、LangChain、AutoGen）
类型安全的代码生成
错误处理和响应验证
Related MCP server: Postman MCP Generator
安装依赖项：
npm install构建服务器：
npm run build通过将以下内容添加到 Claude 设置文件 ( cline_mcp_settings.json ) 来配置 MCP 设置：
{
"mcpServers": {
"postman-ai-tools": {
"command": "node",
"args": [
"/path/to/postman-tool-generation-server/build/index.js"
],
"env": {
"POSTMAN_API_KEY": "your-postman-api-key"
},
"disabled": false,
"autoApprove": []
}
}
}服务器提供了一个名为generate_ai_tool的工具，其参数如下：
{
collectionId: string; // The Public API Network collection ID
requestId: string; // The public request ID
language: "javascript" | "typescript"; // Programming language to use
agentFramework: "openai" | "mistral" | "gemini" | "anthropic" | "langchain" | "autogen"; // AI framework
}// Using the tool through MCP
const result = await use_mcp_tool({
server_name: "postman-ai-tools",
tool_name: "generate_ai_tool",
arguments: {
collectionId: "your-collection-id",
requestId: "your-request-id",
language: "typescript",
agentFramework: "openai"
}
});该工具生成类型安全的代码，包括：
请求/响应的类型定义
错误处理
API 集成
OpenAI 函数定义
文档和示例
安装依赖项：
npm install修改src/index.ts
构建服务器：
npm run build重新启动 Claude 应用程序以加载更新的服务器
POSTMAN_API_KEY ：您的 Postman API 密钥（必需）
该服务器包括针对以下方面的全面错误处理：
参数无效
API 故障
JSON 解析错误
网络问题
错误响应包括详细信息以帮助诊断问题。
欢迎贡献代码！欢迎提交 Pull 请求。
MIT 许可证
Generate code for an AI agent tool using a Postman collection and request
| Name | Required | Description | Default |
|---|---|---|---|
| collectionId | Yes | The Public API Network collection ID | |
| requestId | Yes | The public request ID | |
| language | Yes | Programming language to use | |
| agentFramework | Yes | AI agent framework to use |
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 states the tool 'generate[s] code' but does not clarify aspects like whether this is a read-only operation, if it requires authentication, potential side effects, or output format. This leaves significant gaps in understanding the tool's behavior.
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 a single, clear sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded and every part of the sentence contributes directly to understanding, making it highly concise 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 complexity of a code generation tool with no annotations and no output schema, the description is insufficient. It lacks details on what the generated code includes, how it handles errors, or the format of the output, leaving the agent with incomplete information to use the tool effectively.
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 description coverage is 100%, so the input schema already documents all parameters thoroughly. The description adds no additional meaning beyond what the schema provides, such as examples or usage context for the parameters. This meets the baseline for high schema coverage but does not enhance parameter understanding.
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 with specific verbs ('generate code') and resources ('AI agent tool'), specifying the input sources ('Postman collection and request'). It distinguishes what the tool does without ambiguity, making it immediately understandable.
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 implies usage by mentioning the input sources (Postman collection and request), but it does not provide explicit guidance on when to use this tool versus alternatives, prerequisites, or exclusions. Since there are no sibling tools, the lack of comparative guidance is less critical, but it still lacks detailed context.
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.
v1.0.0generate_ai_toolScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool's purpose is clearly defined and distinct by default.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against for patterns or conventions.
One tool is too few for the server's stated purpose of 'Postman Tool Generation,' which implies a broader scope like generating, managing, or testing tools. A single generation tool feels thin and incomplete for this domain.
The tool surface is severely incomplete for the inferred domain of Postman-based tool generation. There are obvious gaps, such as no tools for listing, editing, deleting, or testing generated tools, which limits agent workflows to a single action.
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