Click on "Install 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: Task 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
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already disclose that the tool is not read-only, is open-world, and is non-idempotent. The description adds minimal context by saying 'fake data', implying non-production use, but does not explain side effects, return values, or replication differences across calls.
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, concise sentence that front-loads the core action. It contains no filler or redundancy, earning its place fully.
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 tool's simplicity (no params, no output schema) and the presence of annotations covering safety traits, the description is largely sufficient. It could mention the return format or that the user is generated in-memory, but these are not critical gaps for such a simple tool.
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?
With zero parameters, the schema is trivially complete. The description doesn't need to elaborate on parameters, and the 'random' nature suggests no input customization, which is adequately covered.
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 function: creating a random user with fake data. The verb 'create' identifies the action, and 'random user' with 'fake data' distinguishes it from creating a specific user, making the purpose unambiguous.
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 for generating test or placeholder data, but it does not explicitly say when to prefer this over the sibling tool 'create-user'. No exclusions or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a new user in the database
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| Yes | |||
| phone | Yes | ||
| address | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey that this is a write operation (readOnlyHint=false) and non-destructive (destructiveHint=false). The description adds no additional behavioral context such as authentication, idempotency details, or side effects beyond basic creation. No contradiction with annotations.
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 concise sentence with no redundancy. It is appropriately minimal and front-loaded.
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?
For a simple create operation with a clear schema and annotations, the description is adequate but lacks usage guidance and contextual details (like duplicate handling or response format). Since there is no output schema, return behavior isn't described, but this is a minor gap.
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 has 0% description coverage and the description does not mention any of the four required parameters. Names like 'email' and 'phone' are self-explanatory, but the description adds no semantic value beyond what the schema already provides.
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 uses a clear verb ('Create') and resource ('user') with location ('in the database'). It does not explicitly contrast with the sibling 'create-random-user', so it doesn't fully distinguish itself, earning a 4.
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?
No guidance is provided on when to use this tool instead of the sibling 'create-random-user'. There is no mention of prerequisites, context, or exclusions. The description only states what it does, not when to choose it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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