JMeter MCP Server
Provides configuration for JMeter path and Java options through environment variables
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., "@JMeter MCP Serverrun the load test at /tests/ecommerce.jmx"
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.
π JMeter MCP Server
This is a Model Context Protocol (MCP) server that allows executing JMeter tests through MCP-compatible clients.
π’ Looking for an AI Assistant inside JMeter? π Check outFeather Wand

π Features
π Execute JMeter tests in non-GUI mode
π₯οΈ Launch JMeter in GUI mode
π Capture and return execution output
Related MCP server: my-mcp-server
π οΈ Installation
Local Installation
Install
uv:Ensure JMeter is installed on your system and accessible via the command line.
β οΈ Important: Make sure JMeter is executable. You can do this by running:
chmod +x /path/to/jmeter/bin/jmeterConfigure the
.envfile, refer to the.env.examplefile for details.
# JMeter Configuration
JMETER_HOME=/path/to/apache-jmeter-5.6.3
JMETER_BIN=${JMETER_HOME}/bin/jmeter
# Optional: JMeter Java options
JMETER_JAVA_OPTS="-Xms1g -Xmx2g"π» MCP Usage
Connect to the server using an MCP-compatible client (e.g., Claude Desktop, Cursor, Windsurf)
Send a prompt to the server:
Run JMeter test /path/to/test.jmxMCP compatible client will use the available tools:
π₯οΈ
execute_jmeter_test: Launches JMeter in GUI mode, but doesn't execute test as per the JMeter designπ
execute_jmeter_test_non_gui: Execute a JMeter test in non-GUI mode (default mode for better performance)
ποΈ MCP Configuration
Add the following configuration to your MCP client config:
{
"mcpServers": {
"jmeter": {
"command": "/path/to/uv",
"args": [
"--directory",
"/path/to/jmeter-mcp-server",
"run",
"jmeter_server.py"
]
}
}
}β¨ Use case
LLM powered result analysis: Collect and analyze test results.
Debugging: Execute tests in non-GUI mode for debugging.
π Error Handling
The server will:
Validate that the test file exists
Check that the file has a .jmx extension
Capture and return any execution errors
Available Tools
2 toolsexecute_jmeter_testC
Execute a JMeter test.
Args: test_file: Path to the JMeter test file (.jmx) gui_mode: Whether to run in GUI mode (default: False)
| Name | Required | Description | Default |
|---|---|---|---|
| test_file | Yes | ||
| gui_mode | No |
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 full burden. It mentions GUI mode but doesn't disclose behavioral traits like whether execution is blocking/non-blocking, what outputs are generated, error handling, or system requirements. The description is minimal and lacks critical operational context.
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 appropriately sized with two sentences and an Args section. It's front-loaded with the core purpose, though the parameter documentation could be more integrated. No wasted words, but structure is basic.
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 2 parameters with 0% schema coverage, no annotations, but an output schema exists, the description is minimally complete. It covers the basic action and parameters but lacks context about execution behavior, sibling differentiation, and error cases that would help an agent use it correctly.
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. It adds basic meaning for both parameters (test_file path and GUI mode default), but doesn't provide format details (e.g., absolute/relative paths), constraints, or implications of GUI mode. This partially compensates but leaves gaps.
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 states 'Execute a JMeter test' which provides a basic verb+resource combination, but it's vague about what execution entails (e.g., running performance tests, generating reports). It doesn't distinguish from sibling 'execute_jmeter_test_non_gui' beyond mentioning GUI mode in parameters.
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 explicit guidance on when to use this tool versus alternatives. The presence of sibling 'execute_jmeter_test_non_gui' suggests alternatives exist, but the description doesn't explain when to choose GUI vs non-GUI modes or other considerations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
execute_jmeter_test_non_guiC
Execute a JMeter test in non-GUI mode.
Args: test_file: Path to the JMeter test file (.jmx)
| Name | Required | Description | Default |
|---|---|---|---|
| test_file | 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 action ('Execute') but lacks details on permissions, side effects (e.g., whether it runs tests destructively), output format, error handling, or rate limits. This is inadequate for a tool that likely performs execution operations with potential impacts.
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 appropriately sized and front-loaded with the core purpose in the first sentence. The Args section is structured but minimal. It avoids waste, though it could be more detailed without losing conciseness. Every sentence serves a purpose, but the overall brevity limits completeness.
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 reduces need to explain return values), no annotations, and low schema coverage, the description is minimally adequate. It covers the basic action and parameter, but lacks behavioral context, usage guidelines, and deeper parameter semantics, leaving gaps for effective agent use.
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. It adds meaning by specifying the parameter 'test_file' as a 'Path to the JMeter test file (.jmx)', clarifying the expected format and file type. However, with only one parameter documented, it provides basic but incomplete context (e.g., no details on path validation or examples).
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 verb ('Execute') and resource ('JMeter test in non-GUI mode'), making the purpose understandable. It distinguishes from the sibling tool 'execute_jmeter_test' by specifying 'non-GUI mode', though it doesn't explicitly contrast their differences. The purpose is specific but could be more explicit about sibling differentiation.
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 the sibling 'execute_jmeter_test', nor does it mention any prerequisites, contexts, or exclusions. Usage is implied by the name and mode specification, but explicit alternatives or conditions are missing, leaving gaps for agent decision-making.
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
execute_jmeter_test - First observed
execute_jmeter_test_non_gui
TDQS
The two tools are essentially identical in purposeβboth execute JMeter testsβwith only a minor difference in GUI mode handling. The second tool's name and description suggest it's a redundant subset of the first, creating clear ambiguity and making it impossible for an agent to reliably choose between them without guessing.
Both tools follow a consistent verb_noun pattern (execute_jmeter_test and execute_jmeter_test_non_gui), which is clear and predictable. The slight deviation in the second tool's name (adding '_non_gui') is logical and maintains readability, though it hints at the redundancy issue rather than a naming inconsistency.
With only two tools, this server feels severely under-scoped for a JMeter testing domain, as it lacks basic operations like listing tests, viewing results, or managing configurations. The redundancy between the tools exacerbates this, making the set appear incomplete and poorly thought-out for practical use.
The tool set is highly incomplete for a JMeter server, covering only test execution and missing essential CRUD operations such as creating, updating, or deleting tests, as well as retrieving results or managing test plans. This will likely cause agent failures when trying to perform common testing workflows beyond a single execution.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yoβ¦
MCP server for building and testing AI agents with multi-model experimentation and insights.
An MCP server that provides access to Testiny projects, test cases and test runs
The Mercado Pago MCP Server implements the Model Context Protocol to provide AI agents and LLMs with access to Mercado Pago's APIs and tools within compatible development environments. It acts as an intermediary that translates Mercado Pago resources into executable functions (tools) that AI applications can invoke to perform actions and automate flows. The server simplifies integration, enables using documentation to implement or improve code, and optimizes operations through natural language interactions without manual implementations.
Related MCP Servers
- FlicenseBqualityFmaintenanceA Model Context Protocol server that allows AI assistants to execute and manage JMeter performance tests through natural language commands.672-
- FlicenseNot gradedqualityDmaintenanceA Model Context Protocol server that allows integration with Claude Desktop by creating and managing custom tools that can be executed through the MCP framework.88-
- FlicenseCqualityDmaintenanceA Model Context Protocol server that enables execution of JMeter performance tests through AI assistants and MCP-compatible clients like Claude, Cursor, and Windsurf.2-
- FlicenseBqualityDmaintenanceA Model Context Protocol (MCP) server that can be deployed locally via stdio or remotely via SSE/HTTP endpoints, supporting multiple MCP clients including VS Code, Cursor, Windsurf, and Claude Desktop.1-
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/Narasimhakatta/MCP-Server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server