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Faneraiy14

ci-watch-mcp

by Faneraiy14
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is zero ambiguity in tool selection. The tool's purpose is clearly defined and distinct.

    Naming Consistency4/5

    With a single tool, naming consistency is trivially maintained. The name 'watch_ci' is descriptive and follows a verb_noun pattern, though no comparison set exists to evaluate full consistency.

    Tool Count5/5

    A single-purpose server with one tool is well-scoped. The tool encapsulates a complete workflow (waiting for CI and returning results), avoiding unnecessary fragmentation.

    Completeness4/5

    The tool covers the core CI-watching workflow effectively, including failure log retrieval. Minor gaps exist (e.g., no explicit cancel or list operations), but they are outside the tool's stated purpose and do not hinder its primary function.

  • Average 4.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 6 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

  • This repository is licensed under MIT License.

  • 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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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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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

  • Behavior4/5

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

    With no annotations, the description carries the burden. It discloses key behaviors: single-call summary return, and immediate failed-step log tail on failure. It does not mention timeout behavior or authentication requirements, but the essential behavioral traits are covered well beyond what the input schema conveys.

    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 three sentences, efficient and front-loaded. The first sentence states the core function, the second details the return behavior, and the third shows the manual cycle it replaces. No filler or redundancy.

    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?

    While there is no output schema and no annotations, the description provides a strong overview of inputs, behavior, and outputs, including failure handling. It does not specify timeout outcomes, but the timeout_ms parameter in the schema partially addresses that. Overall, it gives an agent sufficient context to select and invoke the tool 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?

    Input schema covers 100% of parameters with detailed descriptions, so baseline is 3. The description adds minimal extra parameter context (e.g., 'current or given commit' aligns with ref, 'local git repository' aligns with cwd), but it does not meaningfully enhance the schema's already-complete parameter documentation.

    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 waits for GitHub Actions CI completion and returns a summary (ok/conclusion/url), with failed-step logs on failure. It uses a specific verb ('Чекає' – waits) and identifies the resource and scope (current/given commit in local git repo), making the purpose unambiguous even without sibling tools.

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

    Usage Guidelines5/5

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

    The description explicitly frames the tool as a replacement for the manual 'push -> sleep -> gh run list -> gh run view --log-failed' cycle, clearly indicating when to use it. It provides a concrete alternative scenario and positions this tool as the streamlined single-call solution.

    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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