by sker65
Bridge TestRail with AI tools for smarter, streamlined software testing workflows. Leverage artificial intelligence in s
Connects AI tools directly to TestRail's test management platform, allowing you to query and manage test cases, runs, results, and projects through natural language.
TestRail is a community-built MCP server published by sker65 that provides AI assistants with tools and capabilities via the Model Context Protocol. Bridge TestRail with AI tools for smarter, streamlined software testing workflows. Leverage artificial intelligence in s It is categorized under developer tools, productivity.
You can install TestRail in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
TestRail is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We evaluated TestRail against two servers with overlapping tools; this profile had the clearer scope statement.
We wired TestRail into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
According to our notes, TestRail benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
According to our notes, TestRail benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
We wired TestRail into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
We evaluated TestRail against two servers with overlapping tools; this profile had the clearer scope statement.
TestRail is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
TestRail is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
TestRail has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Useful MCP listing: TestRail is the kind of server we cite when onboarding engineers to host + tool permissions.
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A Model Context Protocol (MCP) server for TestRail that allows interaction with TestRail's core entities through a standardized protocol.
To install testrail-mcp for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @sker65/testrail-mcp --client claude
Clone this repository:
git clone https://github.com/yourusername/testrail-mcp.git
cd testrail-mcp
Create and activate a virtual environment:
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
Install dependencies:
pip install -e .
The TestRail MCP server requires specific environment variables to authenticate with your TestRail instance. These must be set before running the server.
Create a .env file in the root directory of the project:
TESTRAIL_URL=https://your-instance.testrail.io
[email protected]
TESTRAIL_API_KEY=your-api-key
Important Notes:
TESTRAIL_URL should be the full URL to your TestRail instance (e.g., https://example.testrail.io)TESTRAIL_USERNAME is your TestRail email address used for loginTESTRAIL_API_KEY is your TestRail API key (not your password)
Verify that the configuration is loaded correctly:
uvx testrail-mcp --config
This will display your TestRail configuration information, including your URL, username, and the first few characters of your API key for verification.
If you're using this server with a client like Claude Desktop or Cursor, make sure the environment variables are accessible to the process running the server. You may need to set these variables in your system environment or ensure they're loaded from the .env file.
The server can be run directly using the installed script:
uvx testrail-mcp
This will start the MCP server in stdio mode, which can be used with MCP clients that support stdio communication.
In Claude Desktop, add a new server with the following configuration:
{
"mcpServers": {
"testrail": {
"command": "uvx",
"args": [
"testrail-mcp"
],
"env": {
"TESTRAIL_URL": "https://your-instance.testrail.io",
"TESTRAIL_USERNAME": "[email protected]",
"TESTRAIL_API_KEY": "your-api-key"
}
}
}
}
In Cursor, add a new custom tool with the following configuration:
{
"name": "TestRail MCP",
"command": "uvx",
"args": [
"testrail-mcp"
],
"env": {
"TESTRAIL_URL": "https://your-instance.testrail.io",
"TESTRAIL_USERNAME": "[email protected]",
"TESTRAIL_API_KEY": "your-api-key"
}
}
In Windsurf, add a new tool with the following configuration:
{
"name": "TestRail MCP",
"command": "uvx",
"args": [
"testrail-mcp"
],
"env": {
"TESTRAIL_URL": "https://your-instance.testrail.io",
"TESTRAIL_USERNAME": "[email protected]",
"TESTRAIL_API_KEY": "your-api-key"
}
}
For testing and debugging, you can use the MCP Inspector:
npx @modelcontextprotocol/inspector \
-e TESTRAIL_URL=<your-url> \
-e TESTRAIL_USERNAME=<your-username> \
-e TESTRAIL_API_KEY=<your-api-key> \
uvx testrail-mcp
This will open a web interface where you can explore and test all the available tools and resources.
This server is built using:
MIT
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.