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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusing it with another tool. The tool's purpose is clearly described, and its parameters are well-defined.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (verify_proxy_savings), and with only one tool there is no inconsistency to evaluate.

    Tool Count2/5

    The server exposes only one tool, which feels too thin for a platform named TokenTrust that implies a broader verification suite. While the tool is complex, a single tool limits the server's ability to support related operations.

    Completeness3/5

    The tool covers the core verification workflow (standard, cross-tool comparison, live mode, regression diff), but lacks supporting operations such as listing supported proxies, retrieving historical runs, or accessing task corpus details. These are notable gaps that an agent might need.

  • Average 4.8/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 66 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 Apache 2.0.

  • 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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  • 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

  • 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 delivers: it discloses read-only repo behavior, the append of a versioned run record to local history, non-idempotent output, live network access gated by live+confirmCost, and failure/refusal returning isError=true. This far exceeds basic safety disclosure.

    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 long but organized into purpose, usage, side effects, parameters, and return sections. Every paragraph carries necessary context, though it repeats some schema parameter details. Given the tool's complexity and the absence of annotations/output schema, it is appropriately sized and structured.

    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?

    No output schema exists, so the description correctly enumerates the return structure (run_id, records, tt03/tt05 maps). It also covers prerequisites, side effects, failure modes, and live-mode safety, making the tool fully self-contained for an agent to invoke 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?

    Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the array form for TT04 cross-tool comparison, the live/confirmCost gating semantics, example calls, and the headroom-not-yet-runnable nuance. However, it largely restates the schema details rather than introducing substantial new parameter-level semantics.

    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 opens with a specific verb ('Runs an independent, adversarial verification') and names the target resource (AI-coding-agent context-reduction proxy) and the method (TT01-TT05 engine, real tokenizer, bundled corpus). It clearly distinguishes this from a vendor benchmark rerun and from general token counting, which satisfies the top score.

    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?

    Explicitly states 'Call this when an agent needs a trustworthy...' and provides concrete examples ('before recommending a proxy, evaluating a version upgrade, or checking a CI regression'). It also gives exclusions: 'not for general token counting or for proxies outside {rtk, headroom}' and explains the headroom limitation. This is model usage guidance.

    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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TokenTrust-CLI MCP server

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