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ProgrammerDream

coverage-mcp

by ProgrammerDream
OverviewSchemaRelated ServersScoreDiscussions
Python
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.0.1

  • Disambiguation5/5

    The two tools have clearly distinct purposes: compile_project handles Maven compilation with fallback strategies, while coverage_check runs tests and evaluates branch coverage. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent snake_case verb_noun pattern: compile_project and coverage_check. The naming is predictable and clear.

    Tool Count3/5

    With only 2 tools, the server feels minimal. While the tools themselves are substantial and cover core compilation and testing, the count is at the low end of typical MCP servers.

    Completeness2/5

    The server lacks essential features for a complete coverage workflow, such as listing projects, retrieving historical coverage data, or managing configurations. Agents must re-run checks to get results, limiting usability.

  • Average 4/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 11 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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.

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  • 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.

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    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.

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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?

    With no annotations provided, the description carries full burden for behavioral disclosure. It mentions a 'three-level fallback' but does not explain side effects, permissions, error handling, or whether the compilation modifies files. Minimal transparency compared to the ideal for a mutation tool.

    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 concise, with a one-sentence overview followed by structured parameter documentation. Every sentence adds value, though the initial line could be more concise by not including the fallback detail implicitly.

    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 lack of an output schema, the description mentions return values (status and log paths), which is helpful. However, it does not specify the output format, error behavior, or prerequisites like Maven installation. For a compilation tool, this is adequate but not fully complete.

    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?

    The parameter descriptions add significant value beyond the input schema, which has 0% coverage. For 'project_name', it explains accepted formats and resolution priority; for 'workspace_root', it specifies default derivation; for 'strategy', it lists valid values. This fully compensates for the schema's lack of descriptions.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool compiles a project using Maven and returns status and log paths for each step. The verb 'compile' and resource 'project' are specific, and the sibling tool 'coverage_check' has a distinct purpose, providing 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 does not provide any guidance on when to use this tool versus the sibling tool 'coverage_check'. There is no mention of prerequisites, such as requiring Maven to be installed, or context about when compilation is appropriate.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description carries the full burden. It discloses key behaviors: incremental compilation option, JVM fork reuse, auto-collection of tests when package is given, and acceptance of different formats for min_branch. It does not mention permissions or side effects, but for a test runner, this is acceptable.

    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 structured as a clear list of arguments with explanations. While not extremely terse, every sentence adds value. It could be slightly more concise, but it remains readable and informative.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (7 parameters, no output schema), the description covers all essential aspects: parameter descriptions, default behaviors, and return value summary. It is complete enough for an agent to use the tool correctly.

    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?

    Since schema description coverage is 0%, the description must compensate, and it does so thoroughly. It explains the purpose and acceptable formats for each parameter (e.g., tests can include/exclude .java, cover can be empty, package auto-collects). This adds significant value beyond the minimal schema.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states it runs specified test classes and checks branch coverage, returning test results, coverage, and uncovered branch lines. It distinguishes itself from the sibling tool 'compile_project' which only compiles code.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides context for when to use the tool (when needing to run tests with coverage) and details on parameter usage. However, it lacks explicit guidance on when not to use it or direct comparison with alternatives.

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

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