BlazeMeter MCP Server
Provides programmatic access to BlazeMeter's performance testing platform through MCP tools. Enables users to retrieve test runs, analyze performance data, view error reports, and manage testing resources via natural language interactions.
README
BlazeMeter MCP Server
This project provides a Model Context Provider (MCP) server for integrating with BlazeMeter's API, enabling programmatic access to performance test data and management features. The server exposes a set of tools that can be used by MCP-compatible clients (such as Claude Desktop and Cursor) to interact with BlazeMeter resources.
Let's set things up!
🚦 Getting Started
⚙️ Prerequisites
Before starting, please ensure you have:
- Node.js (v18+ required, v20+ recommended)
- npm (included with Node)
Warning: if you run with a lower version of Node, fetch won't be present. Tools use fetch to make HTTP calls. To work around this, you can modify the tools to use node-fetch instead. Make sure that node-fetch is installed as a dependency and then import it as fetch into each tool file.
📥 Installation & Setup
1. Install dependencies
Run from your project's root directory:
npm install
- On Linux/macOS (sh):
which node - On Windows (PowerShell):
Get-Command node | Select-Object -ExpandProperty Source
To check the node version, run:
node --version
To get the absolute path to mcpServer.js, run:
- On Linux/macOS (sh):
realpath mcpServer.js - On Windows (PowerShell):
Resolve-Path mcpServer.js | Select-Object -ExpandProperty Path
👩💻 Connect the MCP Server to Claude Desktop or Cursor
You can connect your MCP server to any MCP client. Below are instructions for connecting it to Claude Desktop and Cursor.
Claude Desktop
Step 1: Note the full path to node and the mcpServer.js from the previous step.
Step 2. Open Claude Desktop → Settings → Developers → Edit Config and add a new MCP server:
{
"mcpServers": {
"<server_name>": {
"command": "<absolute/path/to/node>",
"args": ["<absolute/path/to/mcpServer.js>"]
}
}
}
Restart Claude Desktop to activate this change. Make sure the new MCP is turned on and has a green circle next to it. If so, you're ready to begin a chat session that can use the tools you've connected.
Cursor
Step 1: Note the full path to node and the mcpServer.js from the previous step.
Step 2. In Cursor, open the Command Palette and search for "MCP: Add Server" or go to Settings → MCP Servers.
Step 3. Add a new MCP server with the following configuration:
- Command:
<absolute/path/to/node> - Arguments:
<absolute/path/to/mcpServer.js>
Step 4. Save and enable the server. Cursor will show the MCP server as available, and you can use the BlazeMeter tools directly from the Cursor interface.
Note: For both clients, ensure you use the absolute paths and Node.js v18+.
Warning: If you don't supply an absolute path to a node version that is v18+, the client may fall back to another node version on the system of a previous version. In this case, the fetch API won't be present and tool calls will not work. If that happens, you can a) install a newer version of node and point to it in the command, or b) import node-fetch into each tool as fetch, making sure to also add the node-fetch dependency to your package.json.
Additional Options
🐳 Docker Deployment (Production)
For production deployments, you can use Docker:
1. Build Docker image
docker build -t <your_server_name> .
2. Client Integration
Add Docker server configuration to your MCP client (Claude Desktop or Cursor):
{
"mcpServers": {
"<your_server_name>": {
"command": "docker",
"args": ["run", "-i", "--rm", "--env-file=.env", "<your_server_name>"]
}
}
}
Add your environment variables (API keys, etc.) inside the
.envfile.
The project comes bundled with the following minimal Docker setup:
FROM node:22.12-alpine AS builder
WORKDIR /app
COPY package.json package-lock.json ./
RUN npm install
COPY . .
ENTRYPOINT ["node", "mcpServer.js"]
🌐 Server-Sent Events (SSE)
To run the server with Server-Sent Events (SSE) support, use the --sse flag:
node mcpServer.js --sse
🛠️ Additional CLI commands
List tools
List descriptions and parameters from all generated tools with:
node index.js tools
Example:
Available Tools:
Workspace: acme-workspace
Collection: useful-api
list_all_customers
Description: Retrieve a list of useful things.
Parameters:
- magic: The required magic power
- limit: Number of results returned
[...additional parameters...]
🔥 BlazeMeter API Tools
This project includes tools for interacting with the BlazeMeter API. You must set the following environment variables in your .env file:
Example .env
BASE_URL=https://a.blazemeter.com
BZM_USERNAME=your_blazemeter_api_key
BZM_PASSWORD=your_blazemeter_api_secret
BZM_ACCOUNT_ID=your_blazemeter_account_id # (optional, used by workspace-list)
BZM_WORKSPACE_ID=your_blazemeter_workspace_id # (optional, used by project-list)
- For tools where the parameter is optional, the value will be read from
.envif not provided. - For tools where the parameter is required, you must provide it in the request.
Supported BlazeMeter Endpoints
| Tool Name | Endpoint & Description | Required Parameter(s) |
|---|---|---|
| get_workspaces | /api/v4/workspaces?accountId=...<br>List workspaces for an account |
(optional) accountId |
| get_projects | /api/v4/projects?workspaceId=...<br>List projects for a workspace |
(optional) workspaceId |
| get_test_runs | /api/v4/masters?testId=...<br>List test runs (masters) for a test |
testId |
| get_test_run_summary | /api/v4/masters/{masterId}/reports/default/summary<br>Get summary for a test run |
masterId |
| get_test_run_aggregate_data | /api/v4/masters/{masterId}/reports/aggregatereport/data<br>Aggregate report data |
masterId |
| get_test_run_errors_data | /api/v4/masters/{masterId}/reports/errorsreport/data<br>Errors report data |
masterId |
| get_test_run_thresholds | /api/v4/masters/{masterId}/reports/thresholds<br>Thresholds report |
masterId |
| get_test_run_timeline_kpis | /api/v4/masters/{masterId}/reports/timeline/kpis<br>Timeline KPIs report |
masterId |
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。