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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool targets a distinct condition type: shell command, URL, or file. The purpose of each is clear, and there is no meaningful overlap in their intended use cases. An agent can easily select the right tool based on the resource it needs to wait on.

    Naming Consistency5/5

    All tools follow the consistent verb_noun pattern of 'await_' followed by the target resource (command, url, file). This makes the tool names predictable and easy to remember. The naming convention is uniform throughout the set.

    Tool Count5/5

    With three tools, the server is well-scoped for its purpose of providing wait-for-condition primitives. Each tool covers a distinct and common category of waiting (shell, HTTP, file), so the count feels neither thin nor excessive for the domain.

    Completeness5/5

    The three tools cover the primary methods for blocking on external signals: command exit status, URL availability/response, and file presence/content. This set provides a solid foundation for waiting on common CI/CD, deployment, and build scenarios. While a generic 'sleep' tool is absent, the command-based approach can handle arbitrary delays, making the surface reasonably complete.

  • Average 4.3/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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"
      ]
    }

    Then . Browse examples.

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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

  • Behavior4/5

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

    With no annotations, the description discloses key behavior: blocking, polling at intervals, and the exit-code meaning (0 success, 2 failure, others continue). It does not state what happens on timeout, but the timeout_seconds schema description implies a limit, which is a minor omission.

    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-organized: a clear summary, typed exit-code bullets, a usage note, and an example. Every sentence earns its place, with no fluff 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?

    For a simple 3-parameter tool with no output schema, the description covers purpose, usage, exit semantics, and an example. It could mention timeout behavior, but the schema's 'max wait time' field and the overall clarity make this a solid, near-complete description.

    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 coverage is 100%, so the baseline is 3. The description reiterates the exit-code convention already present in the command schema and provides an example using interval_seconds, but does not add deep new meaning beyond the 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 states a clear action: 'Block agent execution until a shell command exits with code 0'. It specifies the resource (shell command) and distinguishes from sibling tools (await_url, await_file) by focusing on commands. The exit-code convention adds precise scope.

    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?

    It explicitly says 'Use this to wait for long-running operations: cloud builds, CI/CD, deployments, etc.' and gives a concrete example. It does not explicitly contrast with URL/file waiting, but the example and domain make the intended use clear.

    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?

    The description discloses key behavioral traits: blocking execution until condition or timeout, and optional body content checking. It also notes the agent is 'stuck' during the wait, which aligns with the blocking nature. Missing details about failure modes (e.g., unreachable URL) and polling interval behavior, but the schema covers interval.

    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 two terse sentences plus an illustrative example. It front-loads the core behavior without wordiness, and every sentence contributes meaning.

    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 no annotations and no output schema, the description covers the essential behaviors (blocking, timeout, body check) but omits what happens on timeout (e.g., error/return value) and does not reference sibling tools. It is mostly complete but leaves minor gaps.

    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 100% for all five parameters, so the baseline is 3. The description adds an example but does not explain parameters beyond what the schema already provides.

    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's action (block agent execution), the resource (URL), and the specific condition (expected HTTP status code, optional body string). This distinguishes it from sibling await tools that target commands and files.

    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 context for using this tool is clear: wait for a URL to return a specific status. The example further illustrates a health-check scenario. However, it does not explicitly mention alternatives, exclusions, or when to prefer this over await_command/await_file.

    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 provided, the description carries the full burden. It clearly discloses that execution is blocked until a condition is met, which is a critical behavioral trait. It does not mention timeout/error behavior, but the schema provides the timeout_seconds parameter. The example further clarifies the expected usage.

    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?

    Two concise sentences plus an example. Every sentence adds value: it states the core behavior, gives a use case, and demonstrates usage. No redundant information.

    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?

    For a tool with 4 parameters, no output schema, and no annotations, the description covers the purpose, a use case, and an example. It doesn't explain return values on success/failure or timeout behavior, but the schema covers the timing parameters. This is sufficiently complete for a straightforward blocking wait operation.

    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 input schema has 100% coverage with descriptions for all four parameters, so baseline is 3. The description adds value by providing a concrete example (path and contains), illustrating how the parameters work together, and reinforcing that 'contains' is optional.

    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 uses a specific verb 'block' and clearly identifies the resource (file) and condition (exists/contains string). It naturally distinguishes itself from the sibling tools await_command and await_url by focusing on file-based waiting.

    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?

    It explicitly states 'Useful for file-based signaling between processes (e.g., build status files)' giving clear when-to-use context. It does not explicitly exclude other tools, but the resource-specific focus makes the intended use obvious.

    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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