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Server Quality Checklist

67%
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  • Latest release: v0.5.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The tool 'ultrathink' has a clearly defined purpose for complex reasoning tasks, and no other tools exist to cause ambiguity.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'ultrathink' follows a single, consistent pattern with no deviations or mixing of conventions.

    Tool Count2/5

    A single tool is too few for the server's apparent purpose of dynamic and reflective problem-solving, as it suggests a monolithic design that may lack granularity. Typically, such a domain would benefit from multiple specialized tools (e.g., for different reasoning phases or problem types), making this count borderline insufficient.

    Completeness2/5

    The tool surface is severely incomplete for the domain of complex reasoning. While 'ultrathink' covers multi-step analysis, there are obvious gaps such as tools for validating assumptions, summarizing insights, or handling specific sub-tasks like debugging or optimization separately, limiting agent flexibility.

  • Average 4.7/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 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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  • If you are the author, simply .

    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 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?

    With no annotations provided, the description carries the full burden and excels. It discloses key behavioral traits: session management ('session state is maintained in memory'), automatic features ('tool automatically manages thought numbering'), confidence tracking, assumption handling, and workflow details. It adds rich context beyond what a schema alone would provide.

    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 (e.g., 'When to use,' 'Usage notes,' 'Parameter groups,' 'Thinking workflow'), but it is overly verbose. Some details, like the extensive 'Thinking workflow' list, could be condensed without losing clarity. While informative, it exceeds what is strictly necessary for conciseness.

    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 high complexity (16 parameters, no annotations, but with output schema), the description is exceptionally complete. It covers purpose, usage, behavioral details, parameter semantics, and provides an example and workflow. With an output schema present, it appropriately omits return value explanations, focusing on the tool's operation and context.

    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 by grouping parameters (e.g., 'Core params,' 'Session management'), explaining their interrelationships (e.g., 'use together' for revision/branching params), and providing usage context in the 'Thinking workflow' section. This enhances understanding beyond the schema's technical definitions.

    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: 'dynamic and reflective problem-solving through thoughts' and 'analyze problems through a flexible thinking process.' It specifies the verb ('analyze,' 'problem-solving') and resource ('thoughts'), and distinguishes it from simple operations. With no sibling tools, it fully defines its unique role.

    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 guidance on when to use and when not to use the tool, with detailed lists (e.g., 'Breaking down complex problems into steps' for use, 'Simple one-step answers' for not use). It includes proactive instructions ('MUST use this tool proactively for complex reasoning tasks') and covers alternatives implicitly by excluding simple cases.

    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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