What are the prerequisites for running the server?
Node.js 18+, npm, an AWS account (for deployment), a Practera API key, and OAuth client credentials (if using OAuth authentication).
@intersective
基本情報
MCP server for Practera
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"practera": {
"url": "https://your-lambda-url.lambda-url.us-east-1.on.aws/mcp"
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
上のボタンでサーバーのエンドポイントに接続し、ツール一覧を取得できます。
An MCP server that provides access to Practera’s GraphQL API, allowing AI models to query Practera learning data. It is intended for learning designers (author users) who want to analyze and restructure projects and assessments.
Install dependencies with npm install, then start the server in development mode with npm run dev. The server will be available at http://localhost:3000/sse. For deployment, build with npm run build and deploy to AWS Lambda using the Serverless Framework (requires AWS CLI and OAuth configuration). When connecting from an MCP client, provide an API key and region (or OAuth credentials if using OAuth).
Node.js 18+, npm, an AWS account (for deployment), a Practera API key, and OAuth client credentials (if using OAuth authentication).
You can use API key authentication by passing the `apikey` and `region` parameters in each tool call. OAuth 2.1 is supported but noted as “coming soon” for production use.
Currently two tools are available: `mcp_practera_get_project` (get project details) and `mcp_practera_get_assessment` (get assessment details).
All data is retrieved from Practera’s GraphQL API. The server does not store or cache data locally.
The server uses SSE transport and can be run locally on port 3000 or deployed to AWS Lambda using the Serverless Framework.

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