Test Generator MCP Server
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 Generator MCP Servergenerate test scenarios from the user story I just uploaded"
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.
Project Overview
This is a Python-based project that leverages MCP and Claude desktop interface for generating test scenarios derived from the user stories uploaded in Claude desktop.
Project Structure
The project has the following top-level files and directories:
.env: Configuration file for environment variables..git/: Git version control directory (internal use)..gitignore: Specifies files and directories to ignore in Git..python-version: Specifies the Python version for the project..venv/: Python virtual environment directory.README.md: Project overview and documentation.main.py: Main entry point for the application.pyproject.toml: Python project configuration and dependencies.requirements.txt: Project dependencies (legacy format).server.py: Server implementation for the application.uv.lock: Lock file for the UV package manager.
Related MCP server: Claude Code Starter Kit MCP
Contributing
Contributions are welcome! If you'd like to contribute to this project, please fork the repository and submit a pull request with your changes.
License
Available Tools
2 toolsexport_scenariosC
Save generated test scenarios into a JSON file and store in memory.
| Name | Required | Description | Default |
|---|---|---|---|
| scenarios | Yes | ||
| file_name | No | scenarios.json |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions saving to a JSON file and storing in memory, but fails to clarify critical aspects: whether this is a write operation (implied by 'Save'), if it overwrites existing files, what 'in memory' means practically (e.g., persistence, access), or any permissions/rate limits. The description adds minimal context beyond the basic action.
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 directly states the tool's action, output format, and storage. Every word earns its place, and it's appropriately front-loaded with the core purpose.
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 has an output schema (which should document return values), the description's job is reduced. However, with no annotations, 2 parameters (one required), and nested objects in the input, the description is incomplete: it lacks behavioral details (e.g., mutation effects, error handling) and parameter guidance. It's minimally adequate but has clear gaps for a tool that performs data export.
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%, so the description must compensate for undocumented parameters. It implies 'scenarios' as input but doesn't explain its structure or content requirements. It mentions 'JSON file' but doesn't link to the 'file_name' parameter or detail naming conventions. The description adds some meaning (e.g., scenarios are saved as JSON) but insufficiently clarifies the two parameters' roles and expectations.
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 action ('Save') and resource ('generated test scenarios'), specifying the output format ('JSON file') and storage location ('in memory'). It distinguishes from the sibling tool 'parse_document' by focusing on export rather than parsing. However, it doesn't explicitly differentiate scope or limitations beyond the basic operation.
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 versus alternatives. The description lacks context about prerequisites (e.g., scenarios must be generated first), exclusions, or comparisons to other tools. It implies usage for saving scenarios but offers no further direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
parse_documentB
Read a PDF or DOCX and return plain text.
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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 states the tool reads files and returns plain text, but lacks details on error handling (e.g., invalid file paths, unsupported formats), performance (e.g., file size limits, processing time), or side effects (e.g., whether the file is modified). For a tool with no annotations, this is a significant gap in transparency.
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: 'Read a PDF or DOCX and return plain text.' It is front-loaded with the core action and outcome, with zero wasted words. Every part of the sentence contributes essential information, 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 (reading documents) and the presence of an output schema (which likely describes the plain text return), the description is minimally adequate. It covers the basic purpose and input type but lacks details on usage context, behavioral traits, and parameter specifics. With no annotations and incomplete parameter guidance, it meets the baseline but has 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?
The input schema has 1 parameter with 0% description coverage, so the schema provides no semantic details. The description implies the parameter is a file path for PDF or DOCX files, adding some context beyond the schema's bare 'File Path' title. However, it doesn't specify format requirements (e.g., absolute vs. relative paths, supported extensions), so it partially compensates but not fully.
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: 'Read a PDF or DOCX and return plain text.' It specifies the verb ('Read'), resource ('PDF or DOCX'), and outcome ('return plain text'), making the function unambiguous. However, it doesn't explicitly differentiate from the sibling tool 'export_scenarios', which might be a related but distinct operation, so it doesn't reach the highest 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 alternatives. It doesn't mention the sibling tool 'export_scenarios' or any other potential tools for document processing, nor does it specify prerequisites like file accessibility or supported formats beyond PDF/DOCX. This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
- First observed
export_scenarios - First observed
parse_document
TDQS
The two tools have completely distinct purposes: one exports generated test scenarios to a file, while the other parses documents to extract text. There is no overlap in functionality or ambiguity between them.
Both tools follow a verb_noun pattern (export_scenarios, parse_document), which is consistent. However, with only two tools, it's a small sample, but there are no deviations in naming style.
With only two tools, the server feels thin for a 'Test Generator' purpose. It lacks core test generation functionality, such as creating or managing test cases, making the scope incomplete and the count too low.
The server is severely incomplete for test generation. It only includes export and parsing tools, missing essential operations like generating tests, editing scenarios, or running tests, which are critical for the domain.
Maintenance
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