Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Test MCP Server Create a random user"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server for managing user data with support for resources, tools, and prompts.
The server provides two resource endpoints for accessing user data:
All Users (users://all) - Retrieves all users from the database
User Profile (users://{userId}/profile) - Retrieves a specific user's profile by ID
Three tools are available for user management:
create-user - Create a new user with specified details
Parameters: name, email, address, phone
create-random-user - Automatically generate and create a user with fake data
Uses AI sampling to generate realistic user information
generate-fake-user (Prompt) - Generate fake user data based on a given name
Parameter: name
Related MCP server: Data Analysis MCP Server
npm installNode.js (with ES modules support)
Dependencies:
@modelcontextprotocol/sdk
zod
npm startThe server uses stdio transport for communication.
Get all users:
users://allGet specific user profile:
users://123/profileCreate a user:
{
"name": "John Doe",
"email": "john@example.com",
"address": "123 Main St",
"phone": "555-0123"
}Create a random user: No parameters required - automatically generates fake user data using AI sampling.
User data is stored in ./src/data/users.json as a JSON array. Each user object contains:
id (number) - Auto-incremented user ID
name (string) - User's full name
email (string) - Email address
address (string) - Physical address
phone (string) - Phone number
Resources: Query user data via URI schemes
Tools: Perform user management operations
Prompts: Generate templated prompts for user creation
The server is built using the Model Context Protocol SDK and implements:
Resource templates with dynamic URI parameters
Tool definitions with Zod schema validation
AI sampling for generating fake data
File-based persistence
MIT
User IDs are auto-incremented starting from the current user count + 1
All operations return JSON responses
Error handling is implemented for user not found scenarios
The server uses the sampling API to generate realistic fake user data
Create a random user with fake data
Inputs: no named parameters.
Create a new user in the database
Inputs: name (required), email (required), phone (required), address (required).
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 2 tools
The two tools are clearly described: one creates a specified user, the other generates a random one. While both involve creation, the random qualifier distinguishes them, so an agent should not easily confuse them.
Both tools follow a consistent verb_noun pattern (create-user, create-random-user), with the second incorporating a modifier while maintaining the same structure. The naming is uniform and predictable.
With only 2 tools, this is a very thin set, but it could be appropriate for a narrowly scoped server focused solely on user creation. It falls into the borderline range (1-2 tools), neither too many nor too few for the apparent purpose.
The server only offers creation capabilities, lacking essential operations like retrieving, updating, or deleting users. This leaves obvious gaps in the user lifecycle, which would cause agent failures if the server is intended for full user management.
Runnable, versioned lists for agents: tools, prompts and lists as resources. Git under the hood.
Create AgentUI data apps: define tables, read and write records, and run programs on your data.
Self-hosted AI prompt library: prompts, collections, tags, teams, chains. 29 MCP tools for agents.
Zero-key temporary JSON database for agents: one tool call, no signup, no OAuth, no API keys.