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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a distinct purpose: explain, generate, parse, and compute next runs for cron expressions. No overlap or ambiguity.

    Naming Consistency4/5

    Three tools follow the verb_noun pattern (explain_cron, generate_cron, parse_cron), while next_runs is a noun phrase, which is a minor inconsistency.

    Tool Count5/5

    Four tools are appropriate for a focused cron utility, covering the essential operations without being too many or too few.

    Completeness5/5

    The set covers cron-related tasks: explanation, generation, parsing with validation, and computing next run times. No obvious gaps.

  • Average 3.7/5 across 4 of 4 tools scored. Lowest: 3.1/5.

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

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • 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.

  • This repository includes a glama.json configuration file.

  • This server has been verified by its author.

  • Add related servers to improve discoverability.

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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

  • Behavior5/5

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

    No annotations are provided, so the description carries the full burden. It explicitly states that the tool is read-only, idempotent, has no side effects, requires no authentication for basic usage, has rate limits (10/day free, unlimited pro), returns descriptive error messages, and processes data locally without storing or logging input. This comprehensive disclosure fully informs the agent of behavioral traits.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is verbose and repetitive, with overlapping 'Behavior' and 'Behavioral Transparency' sections. The 'When to use' content is irrelevant to the tool's actual purpose. The 'Args' section is generic and wastes space. Critical info (e.g., parameter semantics) is absent while less relevant details (data privacy) are over-emphasized. The structure could be streamlined to front-load essential purpose and parameter guidance.

    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?

    The description covers behavioral aspects like rate limits, authentication, and error handling, which are useful. However, it lacks crucial cron-specific context: it does not explain valid values for minute, hour, etc. (e.g., 0-59), how presets interact with individual fields, or what the output format is (a cron expression string). The output schema exists but its content is not described. Given the tool's simplicity, these gaps reduce completeness.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters1/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With 0% schema description coverage, the description must compensate, but it fails. The 'Args' section lists parameters with generic descriptions like 'The minute to analyze or process.' which are unhelpful for cron generation. No valid values, formats, or interactions (e.g., preset versus individual fields) are explained. The schema itself has no descriptions, and the description adds no meaning.

    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 name 'generate_cron' and the opening sentence 'Generate a cron expression.' clearly state the tool's purpose. However, the description lacks explicit differentiation from sibling tools (explain_cron, parse_cron, next_runs), and the generic 'Args' section introduces confusion by describing parameters as 'analyze or process' rather than specifying cron field values.

    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 includes separate 'When to use' and 'When NOT to use' sections, but their content is generic and misleading. For example, 'Use this tool when you need structured analysis or classification of inputs against established frameworks or standards' does not relate to generating cron expressions. The warning against real-time production decision-making is irrelevant. No guidance is provided on when to use generate_cron versus siblings or how presets interact with individual fields.

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

  • Behavior5/5

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

    With no annotations, the description carries full burden and excels: it explicitly states read-only, stateless, idempotent, rate limits, authentication requirements, error handling, and data privacy. This is comprehensive and actionable.

    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 with clear headings, but has redundancy (e.g., 'Behavior' and 'Behavioral Transparency' overlap). Could be more concise without losing key 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?

    Covers behavioral aspects thoroughly. An output schema exists, so return values need not be explained. However, missing specifics like what components are parsed (e.g., minute, hour, etc.) and example usage, which would be helpful for a parse tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 0%, but the description's 'Args' section merely repeats names and types without adding meaning. For example, 'expression (str): The expression to analyze or process' does not specify it must be a cron expression; 'api_key (str): The api key to analyze or process' is misleading (it's for authentication, not analysis). No examples or format hints are provided.

    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 parses cron expressions into components with validation. This distinguishes it from siblings (explain, generate, next_runs) which have different verbs.

    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 'When to use' section is generic ('structured analysis or classification') and does not explicitly compare to sibling tools like explain_cron or next_runs. The 'When NOT to use' warns against real-time decision-making but lacks alternative recommendations.

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

  • Behavior5/5

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

    Since no annotations are provided, the description carries the full burden. It thoroughly covers side effects (read-only, stateless), authentication, rate limits (free/pro), error handling, idempotency, and data privacy. This exceeds expectations for a tool with no annotations.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is well-structured with headings (Behavior, When to use, etc.) and contains necessary behavioral details. However, it is verbose with redundancy; for example, 'Behavior' and 'Behavioral Transparency' sections overlap on read-only and idempotency points. Could be more concise without losing 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?

    The tool has an output schema, so return values are not needed. The description covers usage context, behavioral traits, and guidelines. The only gap is poor parameter semantics, which is scored separately. Given the complexity of cron calculations and the presence of sibling tools, the description is largely complete except for parameter clarity.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters1/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The 'Args' section repeats parameter names with generic phrases like 'The expression to analyze or process' or 'The count to analyze or process,' adding no meaningful context. For a tool that calculates next cron runs, the description should clarify that 'expression' is a cron expression, 'count' is the number of runs, and 'from_date' is the starting date. Schema coverage is 0%, so the description needed to compensate but failed.

    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 'Calculate next N run times for a cron expression,' which is a specific verb and resource. It distinguishes from siblings (explain_cron, generate_cron, parse_cron) by focusing on calculating future runs rather than explaining, generating, or parsing cron expressions.

    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?

    Explicit 'When to use' and 'When NOT to use' sections provide guidance, suggesting it for structured analysis and warning against real-time production use without human review. However, it lacks direct comparison to sibling tools, which would clarify when to choose this over explain_cron, generate_cron, or parse_cron.

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

  • Behavior5/5

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

    With no annotations provided, the description fully carries the behavioral disclosure burden. It includes a comprehensive 'Behavioral Transparency' section covering side effects (read-only, stateless), authentication (no auth for basic usage), rate limits (10/day free), error handling (structured errors), idempotency (fully idempotent), and data privacy. This exceeds typical detail and aligns with the tool's safety profile.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is well-structured with clear sections (Behavior, When to use, Args, Behavioral Transparency), but it suffers from redundancy. The 'Behavior' section and the 'Behavioral Transparency' section repeat nearly identical information about read-only, stateless, idempotency, rate limits, and authentication. This could be streamlined without loss of 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?

    For a tool that explains cron expressions, the description provides extensive behavioral context but does not mention the output format. However, context signals indicate an output schema exists, so the agent can use that for structure. Given the tool's moderate complexity, the description is largely complete, though a brief note on output would strengthen it.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    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. However, the 'Args' section provides minimal and misleading information. 'expression' is described as 'The expression to analyze or process'—vague and failing to specify it's a cron expression. 'api_key' is described identically as 'to analyze or process', which is incorrect; its role is authentication. The description adds little semantic value beyond the parameter names.

    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 purpose: 'Explain a cron expression in human-readable English.' This is a specific verb+resource combination. Sibling tools (generate_cron, next_runs, parse_cron) have distinct purposes, and the description implicitly differentiates by focusing on explanation rather than generation, prediction, or parsing.

    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 includes 'When to use' and 'When NOT to use' sections, providing context on appropriate scenarios (structured analysis) and exclusions (not for real-time decisions without human review). However, it does not explicitly mention alternative sibling tools for specific cases, leaving the agent to infer distinctions from the tool name and purpose.

    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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