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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    With only a single tool, there is no ambiguity or overlap in the set. The tool's purpose is clearly defined and distinct.

    Naming Consistency5/5

    The tool name 'context_status' is descriptive and follows a consistent style. Since there is only one tool, naming consistency is perfect.

    Tool Count2/5

    The server has exactly one tool, which is considered too few for a functional toolset. It provides a single point of functionality but lacks supporting tools.

    Completeness4/5

    The tool covers the core need of monitoring context usage with clear thresholds, but it lacks additional features like history tracking or configuration, leaving minor gaps.

  • Average 4.9/5 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.

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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

  • Behavior5/5

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

    With no annotations, the description carries the full burden of behavioral disclosure. It details what the tool returns, explains the meaning of the phase thresholds, and implies behavior when transcript_path is omitted (defaults to current session). This provides robust transparency beyond the minimal expectations, with no contradictions.

    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 concise, front-loading the core purpose in the first sentence, followed by return details and usage guidance. It contains no fluff, and each sentence contributes valuable information, making it efficiently structured for quick comprehension.

    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 moderate complexity with one optional parameter and no output schema, the description is complete. It explains expected return values, provides self-management context, and describes parameter behavior, ensuring an agent has sufficient information 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.

    Parameters4/5

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

    The schema provides only type and default for transcript_path, with 0% description coverage. The description compensates well by stating it is optional and used 'to target a specific session file,' which clarifies the parameter's purpose and behavior. Minor gaps remain about the exact format or consequences of omission, but the description adds substantial meaning.

    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 with a specific verb ('Estimate current context window usage and distance to compaction') and describes the key outputs (phase, token estimates, profile range). It is specific and unambiguous, distinguishing itself from any potential related tools even without sibling context.

    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 explains when to use the tool ('Use this to self-manage') and provides actionable thresholds for each phase (open, midstream, narrowing, threshold) with corresponding actions. It also clarifies the optional parameter usage, making it clear when and how to invoke it appropriately.

    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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Glama performs regular codebase and documentation scans to:

  • 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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