remote-mcp-ssojet

# Model Context Protocol (MCP) Server (SSOJet) This is a **Model Context Protocol (MCP)** server powered by **SSOJet** for authentication. Users must first sign in with SSOJet. Once authenticated, they can access and use secure tools such as the `add` tool exposed by this server. --- ## Configuration ### SSOJet Setup 1. Go to your **SSOJet dashboard**. 2. Create a new **Single page web application**. 3. Set the callback URL for local development: ``` http://localhost:8788/callback ``` 4. Note the following details from your app: * **Client ID** * **Client Secret** * **Issuer URL** (e.g. `https://<your-tenant>.auth.ssojet.com/v1/`) These will be used to configure your server. --- ### Set up a KV Namespace This project uses a Cloudflare KV namespace to store token metadata: ```bash wrangler kv:namespace create "OAUTH_KV" ``` Then, add the KV binding to your `wrangler.jsonc`. --- ## Environment Variables The following environment variables must be configured to run the server: | Variable | Description | | ---------------------- | ----------------------------------------------------------------------- | | `SSOJET_CLIENT_ID` | The Client ID from your SSOJet application | | `SSOJET_CLIENT_SECRET` | The Client Secret from your SSOJet application | | `SSOJET_ISSUER` | The issuer URL (e.g. `https://<your-tenant>.auth.ssojet.com/v1/`) | | `SSOJET_SCOPE` | Scopes to request (e.g. `openid profile email`) | | `NODE_ENV` | Use `development` for local development | | `API_BASE_URL` | Not required in this case (unless your tool makes API calls externally) | --- ## Development Create a `.dev.vars` file in the root of your project: ```env SSOJET_CLIENT_ID=<your_ssojet_client_id> SSOJET_CLIENT_SECRET=<your_ssojet_client_secret> SSOJET_ISSUER=https://<your-tenant>.auth.ssojet.com/v1/ SSOJET_SCOPE="openid profile email" NODE_ENV=development ``` Then run the MCP server locally: ```bash npm run dev ``` --- ## Tool Available The server currently provides a single tool: | Tool | Description | | ----- | ------------------------------------------------------------- | | `add` | Adds two numbers together. Useful for simple math operations. | Once the user signs in via SSOJet, this tool becomes accessible through compatible MCP clients such as the Workers AI LLM Playground. --- ## Testing with MCP Inspector You can test your server locally with [MCP Inspector](https://playground.ai.cloudflare.com/): 1. Set the **Transport** to `sse` 2. Set the **URL** to: ``` http://localhost:8788/sse ``` 3. A popup will appear for SSOJet authentication 4. Once logged in, you’ll see the available tools --- ## Deploying to Cloudflare Before deploying, set the necessary secrets in your Cloudflare environment: ```bash wrangler secret put SSOJET_CLIENT_ID wrangler secret put SSOJET_CLIENT_SECRET wrangler secret put SSOJET_ISSUER wrangler secret put SSOJET_SCOPE ``` Deploy with: ```bash npm run deploy ``` Then, in the **SSOJet dashboard**, add your deployed callback URL: ``` https://mcp-ssojet-oidc.<your-subdomain>.workers.dev/callback ``` To use the deployed server with MCP Inspector or the LLM Playground, use this endpoint: ``` https://mcp-ssojet-oidc.<your-subdomain>.workers.dev/sse ``` --- ## Troubleshooting ### Cloudflare Worker Logs You can inspect logs and errors using Cloudflare’s observability dashboard: 🔗 [Cloudflare Workers Logs](https://developers.cloudflare.com/workers/observability/logs/) ### SSOJet Logs Visit your SSOJet dashboard and check the **Logs** section to diagnose authentication issues. --- ## Common Issues * ❌ **Invalid credentials**: Double-check that secrets match your SSOJet application. * ❌ **Missing callback URL**: Ensure all callback URLs are added in your SSOJet dashboard. * ❌ **Tool not showing**: Make sure you're authenticated and using the correct endpoint. * ❌ **Local connection failed**: Ensure the MCP server is running on `http://localhost:8788`. ---