PostmanのコレクションとリクエストからAIエージェントツールを生成するMCPサーバー。このサーバーはPostman APIと統合し、APIエンドポイントを様々なAIフレームワークで使用できる型安全なコードに変換します。
モデルコンテキストプロトコル（MCP）は、大規模言語モデル（LLM）と外部システム間のコンテキストを管理するための新しい標準化プロトコルです。このリポジトリでは、 Postmanツール生成API用のインストーラーとMCPサーバーを提供します。
これにより、 Claude DesktopやClineなどの MCP クライアントを使用して、自然言語で Postman アカウントで操作を実行できるようになります。例:
Create an AI tool for: collectionID: 12345-abcde requestID: 67890-fghij typescript openai
Postman コレクションから TypeScript/JavaScript コードを生成する
複数の AI フレームワーク (OpenAI、Mistral、Gemini、Anthropic、LangChain、AutoGen) のサポート
型安全なコード生成
エラー処理と応答検証
Related MCP server: Postman MCP Generator
依存関係をインストールします:
npm installサーバーを構築します。
npm run buildClaude 設定ファイル ( cline_mcp_settings.json ) に次のコードを追加して、MCP 設定を構成します。
{
"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": []
}
}
}サーバーは、次のパラメータを持つgenerate_ai_toolという単一のツールを提供します。
{
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"
}
});このツールは、次の内容を含むタイプセーフなコードを生成します。
リクエスト/レスポンスの型定義
エラー処理
API統合
OpenAI関数定義
ドキュメントと例
依存関係をインストールします:
npm installsrc/index.tsに変更を加える
サーバーを構築します。
npm run build更新されたサーバーをロードするには、Claudeアプリを再起動してください。
POSTMAN_API_KEY : Postman API キー（必須）
サーバーには、次の包括的なエラー処理が含まれています。
無効なパラメータ
API障害
JSON解析エラー
ネットワークの問題
エラー応答には、問題の診断に役立つ詳細なメッセージが含まれます。
貢献を歓迎します！お気軽にプルリクエストを送信してください。
MITライセンス
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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