Skip to main content
Glama

MCP Prompts Server

# MCP Prompts Server - MCP Mode The MCP Prompts server can run in two modes: 1. **HTTP Mode** - Traditional REST API server 2. **MCP Mode** - Model Context Protocol server for AI assistants ## MCP Server Features ### Available Tools 1. **add_prompt** - Add a new prompt to the collection 2. **get_prompt** - Get a prompt by its ID 3. **list_prompts** - List all available prompts with optional filtering 4. **update_prompt** - Update an existing prompt 5. **delete_prompt** - Delete a prompt by its ID 6. **apply_template** - Apply variables to a template prompt ### Tool Usage Examples #### Adding a Prompt ```json { "name": "Code Review Assistant", "content": "Please review this code for best practices and potential issues: {{code}}", "isTemplate": true, "tags": ["code-review", "assistant"], "variables": [ { "name": "code", "description": "The code to review", "required": true, "type": "string" } ] } ``` #### Listing Prompts ```json { "tags": ["assistant"], "search": "code" } ``` #### Applying Template Variables ```json { "id": "prompt_123", "variables": { "code": "function hello() { console.log('Hello World'); }" } } ``` ## Installation & Usage ### Local Development 1. **Install dependencies:** ```bash pnpm install ``` 2. **Build the project:** ```bash pnpm run build ``` 3. **Start in MCP mode:** ```bash pnpm run start:mcp ``` ### Docker 1. **Build MCP Docker image:** ```bash pnpm run docker:build:mcp ``` 2. **Run with Docker Compose:** ```bash pnpm run docker:up:mcp ``` 3. **View logs:** ```bash pnpm run docker:logs:mcp ``` ### Cursor Integration 1. **Configure Cursor MCP:** Add to `.cursor/mcp.json`: ```json { "mcpServers": { "mcp-prompts": { "command": "node", "args": ["dist/index.js"], "env": { "MODE": "mcp" } } } } ``` 2. **Restart Cursor** to load the MCP server 3. **Use in Cursor:** - The MCP tools will be available in Cursor's AI assistant - You can ask Cursor to manage prompts using natural language ## Environment Variables - `MODE` - Set to `mcp` for MCP server mode (default: `http`) - `NODE_ENV` - Environment mode (development/production) - `LOG_LEVEL` - Logging level (debug/info/warn/error) ## Data Storage Currently, the MCP server uses in-memory storage. Prompts are lost when the server restarts. For production use, consider implementing persistent storage adapters. ## Testing with MCP Inspector 1. **Install MCP Inspector:** ```bash npm install -g @modelcontextprotocol/inspector ``` 2. **Test the server:** ```bash mcp-inspector --command "node dist/index.js" --env MODE=mcp ``` ## Development ### Adding New Tools 1. Add tool definition in `src/mcp-server.ts` 2. Implement the handler function 3. Add proper validation using Zod schemas 4. Test with MCP Inspector ### Error Handling All tools include proper error handling and logging. Errors are returned to the client with descriptive messages. ## Troubleshooting ### Common Issues 1. **Server won't start in MCP mode:** - Check that `MODE=mcp` environment variable is set - Verify all dependencies are installed - Check logs for specific error messages 2. **Tools not available in Cursor:** - Restart Cursor after updating `.cursor/mcp.json` - Verify the MCP server is running - Check Cursor's MCP server logs 3. **Build errors:** - Run `pnpm run build:clean` to clean and rebuild - Check TypeScript configuration - Verify all imports are correct ## Contributing 1. Fork the repository 2. Create a feature branch 3. Implement your changes 4. Add tests if applicable 5. Submit a pull request ## License MIT License - see LICENSE file for details.

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sparesparrow/mcp-prompts'

If you have feedback or need assistance with the MCP directory API, please join our Discord server