JMeter MCP Server

by ebadAhmed10

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes: analyzing results, executing tests, generating visualizations, and identifying bottlenecks. However, 'analyze_jmeter_results' and 'get_performance_insights' could be confused as both provide analysis from test results, though one focuses on metrics and the other on recommendations.
Naming Consistency5/5
All tool names follow a consistent snake_case pattern with clear verb_noun structures, such as 'execute_jmeter_test' and 'generate_visualization'. The naming is uniform and predictable across all six tools.
Tool Count5/5
With 6 tools, the server is well-scoped for JMeter testing, covering execution, analysis, visualization, and bottleneck identification. Each tool serves a clear purpose without being overly sparse or bloated.
Completeness4/5
The tool set covers key aspects of JMeter testing: execution (with GUI and non-GUI options), result analysis, visualization, and bottleneck insights. A minor gap is the lack of tools for test creation or editing, but core workflows are well-supported.
Average 3.2/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
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 'execute' implying a mutation, but lacks details on permissions, side effects, or behavior (e.g., what happens during execution, error handling). The GUI mode hint is minimal and doesn't cover execution outcomes or constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with a clear purpose sentence, followed by parameter details in a structured format. It's efficient with minimal waste, though the parameter section could be more integrated into the flow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters with 0% schema coverage and no annotations, the description is incomplete—it lacks behavioral context and usage guidelines. However, an output schema exists, so return values needn't be explained, raising it slightly above minimal adequacy.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 by explaining parameters (e.g., 'Path to the JMeter test file (.jmx)'), but doesn't fully detail usage or constraints beyond defaults. This provides some value but leaves gaps, aligning with the baseline for partial compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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', making the purpose evident. However, it doesn't differentiate from sibling tools like 'execute_jmeter_test_non_gui', which appears to be a similar execution tool with a different mode.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 such as 'execute_jmeter_test_non_gui' or other siblings like 'analyze_jmeter_results'. The description only lists parameters without contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 returns 'insights and recommendations in a formatted string,' which gives some output context, but lacks details on processing behavior (e.g., analysis depth, error handling, performance implications). For a tool with no annotations, this is a significant gap in transparency about how it operates.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated first. The 'Args' and 'Returns' sections add structure without redundancy. However, the inclusion of 'str:' in the return statement is slightly verbose, as the output schema already indicates the return type, making this minor repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter with 0% schema coverage and an output schema present, the description is moderately complete. It explains the parameter and return format, but lacks behavioral details (e.g., how insights are generated, error cases). With no annotations and siblings offering similar functionality, more context on uniqueness or limitations would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the schema provides no parameter details. The description adds value by explaining 'jtl_file' as 'Path to the JTL file containing test results,' clarifying its purpose and format. However, with only one parameter documented, it meets the baseline for minimal compensation but doesn't fully address potential complexities (e.g., file format specifics, path requirements).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get insights and recommendations for improving performance based on JMeter test results.' It specifies the verb ('Get'), resource ('insights and recommendations'), and source ('JMeter test results'). However, it doesn't explicitly differentiate from siblings like 'analyze_jmeter_results' or 'identify_performance_bottlenecks', which likely serve similar purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With siblings like 'analyze_jmeter_results' and 'identify_performance_bottlenecks' that seem related, there's no indication of when this tool is preferred, what prerequisites exist, or when not to use it. The only implied context is having JMeter test results, but that's insufficient for distinguishing among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions analyzing results and returning a formatted string, but lacks details on permissions, rate limits, error handling, or whether it modifies data. For a tool with no annotation coverage, this is insufficient to inform safe and effective use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded, starting with the core purpose followed by parameter explanations. Every sentence adds value without redundancy, and the formatting with 'Args:' and 'Returns:' sections enhances readability. It's appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, 0% schema coverage, and an output schema present (which handles return values), the description is moderately complete. It covers the basic purpose and parameters but lacks behavioral context and usage guidelines. For a tool with siblings and potential complexity in performance analysis, it should do more to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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 meaningful context: 'jtl_file: Path to the JTL file containing test results' and 'detailed: Whether to include detailed analysis (default: False)'. This clarifies parameter purposes beyond the schema's basic types, though it could elaborate on file format or detailed analysis specifics. With 0% coverage, this is strong but not exhaustive.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Analyze JMeter test results and provide a summary of key metrics and insights.' It specifies the verb ('analyze') and resource ('JMeter test results'), but doesn't explicitly differentiate from siblings like 'get_performance_insights' or 'identify_performance_bottlenecks', which might offer overlapping functionality. This makes it clear but not fully distinguished.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With siblings such as 'execute_jmeter_test', 'generate_visualization', 'get_performance_insights', and 'identify_performance_bottlenecks', there's no indication of context, prerequisites, or exclusions. This lack of comparative guidance leaves the agent to guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. While it mentions the tool analyzes JTL files and returns formatted string results, it doesn't describe what constitutes a 'bottleneck', what analysis methods are used, whether the tool modifies files, what permissions are needed, or any performance characteristics. For a tool with no annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear purpose statement followed by well-organized Args and Returns sections. Every sentence serves a distinct purpose: the first establishes the tool's function, while the subsequent sections document parameters and return values without redundancy. The formatting enhances readability while maintaining brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 handles return value documentation) but zero annotation coverage and only basic parameter documentation, the description is minimally adequate. It covers the core purpose and parameter meaning, but doesn't address behavioral aspects like analysis methodology, error conditions, or integration with sibling tools. For a performance analysis tool with multiple similar siblings, more contextual guidance would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate for the schema's lack of parameter documentation. The description explicitly defines 'jtl_file' as 'Path to the JTL file containing test results', which provides essential semantic context beyond the schema's basic string type. However, it doesn't elaborate on file format requirements, path conventions, or example values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Identify performance bottlenecks in JMeter test results.' This specifies both the verb ('identify') and resource ('performance bottlenecks in JMeter test results'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'analyze_jmeter_results' or 'get_performance_insights', which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With siblings like 'analyze_jmeter_results' and 'get_performance_insights' available, there's no indication of what distinguishes this bottleneck identification tool from those other analysis tools. No prerequisites, exclusions, or comparative context is mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 generates visualizations and returns a file path, but lacks details on permissions, side effects (e.g., file overwriting), error handling, or performance implications. For a tool with three parameters and no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded: the first sentence states the purpose, followed by clear sections for Args and Returns. Each sentence adds value without redundancy, making it easy to scan and understand quickly. The format is efficient and avoids unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no annotations) and the presence of an output schema (which covers the return value), the description is reasonably complete. It explains what the tool does and what each parameter means, though it could improve by detailing when to use it versus siblings and adding behavioral notes like file handling or error conditions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context for all three parameters: it explains that 'jtl_file' contains test results, 'visualization_type' specifies the kind of visualization, and 'output_file' is where to save it. With 0% schema description coverage, this compensates well by clarifying each parameter's role, though it doesn't detail allowed values for 'visualization_type' beyond the listed examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate visualizations of JMeter test results.' It specifies the verb ('generate') and resource ('visualizations of JMeter test results'), making the action concrete. However, it doesn't explicitly distinguish this tool from sibling tools like 'analyze_jmeter_results' or 'get_performance_insights', which might also involve result processing or visualization.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 sibling tools like 'analyze_jmeter_results' or 'identify_performance_bottlenecks', nor does it specify prerequisites, such as needing a JTL file from a prior test execution. Without this context, users might struggle to choose the right tool among the available options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool 'supports JMeter properties' and describes default values for parameters, but lacks details on execution behavior (e.g., whether it runs synchronously/asynchronously, error handling, or output implications). It does not contradict annotations, but offers only basic 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. It starts with a concise purpose statement, followed by a bullet-point-like 'Args' section that efficiently documents parameters. Every sentence earns its place, though it could be slightly more front-loaded with key behavioral details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, nested objects) and lack of annotations, the description does a good job explaining parameters and basic operation. Since an output schema exists, it need not detail return values. However, it could improve by adding more behavioral context (e.g., execution flow or error scenarios) to fully compensate for missing annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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 fully. It provides clear explanations for all 5 parameters: 'test_file' (path to .jmx file), 'properties' (dictionary for -J flag), 'generate_report' (boolean for dashboard), 'report_output_dir' (output folder), and 'log_file' (JTL file name). This adds significant meaning beyond the bare schema, covering all parameters effectively.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Execute a JMeter test in non-GUI mode - supports JMeter properties.' It specifies the verb ('Execute'), resource ('JMeter test'), and mode ('non-GUI'), but does not explicitly differentiate it from sibling tools like 'execute_jmeter_test' (which might be GUI mode or have other differences).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 does not mention sibling tools like 'execute_jmeter_test' (which might be for GUI mode) or 'analyze_jmeter_results' (for post-execution analysis), leaving the agent to infer usage context based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![jmeter-mcp MCP server](https://glama.ai/mcp/servers/ebadAhmed10/jmeter-mcp/badges/card.svg)](https://glama.ai/mcp/servers/ebadAhmed10/jmeter-mcp)

Score Badge

Copy to your README.md:

[![jmeter-mcp MCP server](https://glama.ai/mcp/servers/ebadAhmed10/jmeter-mcp/badges/score.svg)](https://glama.ai/mcp/servers/ebadAhmed10/jmeter-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

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/ebadAhmed10/jmeter-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server