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Server Quality Checklist

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  • Latest release: v1.0.1

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool has a single, clearly defined purpose: refining backlog items into structured work items.

    Naming Consistency5/5

    There is only one tool name, 'refine_backlog', which follows a clear verb_noun pattern. Since there are no other tools to compare against, consistency is inherently perfect.

    Tool Count2/5

    A single tool is too few for a server that appears to handle backlog refinement, as it lacks complementary operations like listing, updating, or managing refined items. This minimal scope will likely cause agent failures due to incomplete workflows.

    Completeness2/5

    The tool surface is severely incomplete for backlog management. While the refine_backlog tool performs a specific transformation, there are no tools for creating, retrieving, updating, or deleting backlog items, leaving significant gaps in the domain coverage.

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

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

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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.

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  • This repository includes a glama.json configuration file.

  • This server has been verified by its author.

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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 provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the transformation process (from messy to structured), output format details, tier-based rate limits (items per request), authentication/licensing requirements (license key for higher tiers), and default values for boolean parameters. It doesn't mention error handling or response time, but covers most critical aspects 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 appropriately front-loaded with the core purpose, but contains some redundancy (license key information appears twice) and could be more streamlined. The licensing details and URL reference, while important, add length. Most sentences earn their place, but the structure could be tighter with better grouping of related 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?

    Given the complexity (5 parameters, no output schema, no annotations), the description provides substantial context: it explains the transformation process, output structure, tier limits, prerequisites (questions to ask), and licensing. However, without an output schema, it doesn't fully describe the return format (only lists fields without structure details), and some behavioral aspects like error conditions are missing. For a tool with this complexity, it's quite complete but has minor gaps.

    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?

    Schema description coverage is 100%, so the baseline is 3. The description adds significant value beyond the schema: it explains the purpose of the 'items' parameter ('raw backlog item strings to refine'), provides concrete examples for 'context', clarifies the relationship between 'licenseKey' and environment configuration, and gives formatting details for 'useUserStories' and 'useGherkin' that go beyond the schema's descriptions. However, it doesn't fully explain the semantics of all parameters (e.g., what 'T-shirt size estimate' means in practice).

    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: 'Refine messy backlog items into structured, actionable work items' with specific outputs listed (clean title, problem statement, acceptance criteria, T-shirt size estimate, priority with rationale, tags, optional assumptions). It uses specific verbs ('refine', 'returns') and resources ('backlog items', 'work items'), and since there are no sibling tools, it doesn't need to differentiate from them.

    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 provides explicit usage guidelines: it instructs the agent to ask two specific questions before calling the tool if the user hasn't already specified them (about user stories and Gherkin format), and explains how to set parameters based on user answers. It also details tier limits (Free: 5 items, Pro: 25, Team: 50) and when to use the licenseKey parameter versus environment configuration.

    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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  • Evaluate tool definition quality.

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