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., "@Sample Data MCP generate 10 test records with customer ID, name, status, and signup date"
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 that generates fixed-length test data based on field specifications. This tool helps developers create realistic test datasets with customizable field types and formats.
Generate fixed-length test data records
Support for multiple field types:
string: Random names using Faker library
enum: Random selection from provided values
integer: Random integers within specified range
date: Random dates with customizable format
filler: Space padding fields
Configurable field length and constraints
MCP-compatible for integration with Claude Code
Related MCP server: mockhero
Python 3.13 or higher
Clone or download this project
Navigate to the project directory
Install dependencies:
uv syncAdd the MCP server to your Claude Code configuration. Edit your MCP settings file (typically ~/.config/claude-code/mcp_servers.json or similar):
{
"mcpServers": {
"sample-data-mcp": {
"command": "uv",
"args": ["run", "/path/to/sample-data-mcp/main.py"],
"cwd": "/path/to/sample-data-mcp"
}
}
}Restart Claude Code to load the new MCP server
Once installed, you can use the generate_test_data_tool through Claude Code to create test data:
fields = [
{
"name": "customer_id",
"type": "integer",
"length": 8,
"min": 1000,
"max": 9999
},
{
"name": "customer_name",
"type": "string",
"length": 25
},
{
"name": "status",
"type": "enum",
"length": 6,
"values": ["ACTIVE", "INACTIVE", "PENDING"]
},
{
"name": "signup_date",
"type": "date",
"length": 8,
"format": "%Y%m%d"
},
{
"name": "filler",
"type": "filler",
"length": 5
}
]Type | Description | Required Fields | Optional Fields |
| Random names |
| - |
| Random selection from list |
| - |
| Random integer |
|
|
| Random date |
|
|
| Space padding |
| - |
uv run mcp dev main.pyfaker: For generating realistic fake data
mcp: Model Context Protocol implementation
pydantic: Data validation and settings management
This project is provided as-is for educational and development purposes.
Generate fixed-length test data based on a field specification
| Name | Required | Description | Default |
|---|---|---|---|
| fields | Yes | ||
| num_records | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'fixed-length' as a constraint, but fails to describe critical behaviors: whether the tool is read-only or mutating, what the output looks like (though an output schema exists), error handling, or performance aspects like rate limits. This leaves significant gaps for an agent.
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, efficient sentence with zero waste. It is front-loaded with the core purpose and avoids redundancy. Every word earns its place, making it highly concise and well-structured.
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 moderate complexity (2 parameters with nested FieldSpec objects) and no annotations, the description is incomplete—it lacks behavioral and parameter details. However, the presence of an output schema mitigates the need to explain return values. This results in a baseline adequacy but with clear gaps.
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?
Schema description coverage is 0%, meaning parameters are undocumented in the schema. The description adds minimal value by implying 'field specification' relates to the 'fields' parameter and 'fixed-length' hints at 'length' in FieldSpec, but it does not explain 'num_records', data types, or other schema details. It inadequately compensates for the coverage gap.
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 purpose: 'Generate fixed-length test data based on a field specification.' It specifies the verb ('generate'), resource ('test data'), and key constraint ('fixed-length'). However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, preventing a perfect score.
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 provides no guidance on when to use this tool versus other methods or tools. It lacks context about prerequisites, typical scenarios, or exclusions. Without sibling tools, it could at least hint at use cases like testing or data simulation, but it does not.
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. Dates show when Glama detected each change.
v0.1.0generate_test_data_toolWith only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly distinct as it is the sole tool available.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'generate_test_data_tool' follows a clear verb_noun pattern, but consistency cannot be assessed across multiple tools.
A single tool is generally too few for a server's purpose, as it limits functionality and suggests an incomplete or overly narrow scope. For a data generation server, one tool is insufficient to cover typical needs like varied data types or configurations.
The server's domain appears to be test data generation, but with only one tool, the surface is severely incomplete. It lacks operations for different data formats, validation, customization, or management, which are essential for comprehensive data handling.
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