# TestRail

> Use this tool when you need to integrate AI assistants with your software testing workflows, enabling interaction with test management data such as projects, test cases, and results. It solves problems of manual data transfer and synchronization between AI tools and test management platforms, providing a standardized interface for MCP clients. The tool takes in authentication credentials and MCP requests, and outputs formatted test management data, making it ideal for teams seeking to automate testing workflows.

Canonical page: https://skillsregistry.net/skills/sker65-testrail  
JSON: https://api.skillsregistry.net/v1/skills/sker65-testrail

## Description

TestRail MCP Server provides a bridge between AI assistants and TestRail's test management platform, enabling interaction with projects, test cases, runs, results, and datasets through standardized MCP tools and resources. Developed by Stefan Rinke, this Python implementation uses FastMCP to expose TestRail's API functionality, making it easy for MCP clients like Claude Desktop, Cursor, and Winsurf to access and manipulate test management data. The server handles authentication, request formatting, and response parsing behind the scenes, making it valuable for teams who need to integrate AI assistants with their software testing workflows.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** security
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/sker65-testrail)
- **Repository:** <https://github.com/sker65/testrail-mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "sker65-testrail"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/sker65-testrail` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/sker65-testrail/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt