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., "@MCP_CreateUser Create user John Doe, john@example.com, 123 Main St, 555-1234"
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 with a create-user tool that persists users to a local JSON file, plus a client for testing it.
This project demonstrates a minimal MCP server built with the
@modelcontextprotocol/sdk
package. It exposes a single tool — create-user — that accepts a name, email,
address, and phone, then appends the record to data/users.json.
Related MCP server: Data Analysis MCP Server
.
├── index.js # MCP server entry point
├── package.json # Scripts and dependencies
├── package-lock.json
├── .gitignore
├── README.md
└── data/
└── users.json # Persistent user store (created on first run)Node.js 18+
npm
npm installStart the server in STDIO mode (the transport used by MCP clients and the Inspector):
npm run server:devThe MCP Inspector lets you list and call tools from a web UI:
npm run server:inspectIt opens http://127.0.0.1:6274 automatically. Set Transport Type to STDIO,
Command to npm, and Arguments to run server:dev, then click
Connect.
create-userCreate a new user in the database.
Parameters
Name | Type | Required | Description |
name | string | yes | Full name of the user |
string | yes | Email address | |
address | string | yes | Postal address |
phone | string | yes | Phone number |
Example call
{
"name": "Alice",
"email": "alice@example.com",
"address": "123 Main St",
"phone": "555-1234"
}Result
{
"content": [
{
"type": "text",
"text": "User 5 created successfully"
}
]
}Users are stored in data/users.json as an array of objects with an auto-incrementing
id.
ISC
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 the safety profile (readOnlyHint false, destructiveHint false, idempotentHint false), and the description only restates the obvious 'create' operation, adding no extra context such as authentication needs, side effects on duplicate emails, or return behavior. This is a missed opportunity to disclose meaningful behavioral traits beyond structured data.
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, front-loaded sentence that states the core purpose without wasted words. It is appropriately concise for a simple tool, though it lacks substance that could be included without becoming verbose.
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 no output schema, four required parameters, and a mutation operation, the description should at least mention what the tool returns upon success, how errors (e.g., duplicate email) are handled, or any required permissions. None of this is present, leaving significant gaps for an agent to make safe and correct calls.
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 0% schema description coverage and no parameter details in the description, the agent receives no semantic information about name, email, address, or phone beyond their obvious string names. The description does not compensate for the schema's silence, failing to explain formats, constraints, or required combinations.
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 'Create a new user in the database' uses a specific verb and resource, clearly indicating a create operation. It does not explicitly differentiate from the sibling tool 'create-random-user', though the meaning is implicitly distinct (this creates a specific user, the sibling creates a random one).
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 explicit guidance is provided about when to use this tool versus create-random-user. The description implies a use case for adding a specific user, but it does not state conditions, prerequisites, or mention the alternative, leaving the decision to the agent's inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0create-userScored across 1 tool
There is only one tool, so there is no possibility of confusion or overlap with other tools. The single tool's purpose is clearly stated.
The tool name 'create-user' follows a clear verb-object convention. With only one tool, there are no inconsistent naming patterns to detract from coherence.
One tool is purpose-aligned for a server named MCP_CreateUser, but it still feels thin compared to typical 3-15 tool servers. There is no surrounding surface for related operations.
Only creation is exposed; there is no get, list, update, or delete for users. Any workflow that needs to verify, retrieve, or manage the created user will hit a dead end, making the surface significantly incomplete.
Generate synthetic random user data for testing, demos, and development without using real persona.
Host AI-generated single-file HTML apps with JSON persistence. No signup.
Create, configure and manage an AI phone receptionist. Requires a Fonixia account.
Verify addresses, email addresses, and phone numbers with confidence scores.
