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Narasimhakatta

JMeter MCP Server

by Narasimhakatta
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Python
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Server Quality Checklist

42%
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  • Latest release: v1.0.0

  • Disambiguation1/5

    The two tools are essentially identical in purpose—both execute JMeter tests—with 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.

    Naming Consistency4/5

    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.

    Tool Count2/5

    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.

    Completeness2/5

    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.

  • Average 2.8/5 across 2 of 2 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

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  • This repository includes a README.md file.

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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 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.

    Conciseness4/5

    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.

    Completeness3/5

    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.

    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 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.

    Purpose3/5

    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.

    Usage Guidelines2/5

    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.

  • 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 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.

    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 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.

    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 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.

    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 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.

    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 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.

    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 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.

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