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ericutility-prog

Verified Burst

by ericutility-prog
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or ambiguity between tools. The tool's purpose is singular and clearly defined.

    Naming Consistency5/5

    The single tool 'buy_verified_burst' follows a consistent verb_noun pattern that aligns with the server name and clearly conveys its action.

    Tool Count3/5

    The server has only one tool, which feels minimal; however, for a very narrow, specific service, this count may be acceptable but is at the lower boundary of reasonable scope.

    Completeness5/5

    The tool 'buy_verified_burst' encapsulates the entire intended workflow (buy, escalate, verify, charge) for verified inference bursts, leaving no obvious gaps for its stated purpose.

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

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

    • No community issues in the last 6 months
    • 55 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

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  • This repository includes a README.md file.

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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, the description fully carries the burden. It reveals key behaviors: escalation to fast silicon, best-of-N sampling, verification gating, conditional charging 'ONLY if it passes', returns verified answer + receipt, and budget cap. No contradictions or missing critical traits.

    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 highly concise—four sentences, each earning its place. Front-loaded with the main purpose, it efficiently covers purpose, process, output, and usage context without redundancy.

    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 tool's complexity (5 parameters, no output schema, no annotations), the description provides a solid mental model: it explains the flow and output. However, it omits details about the answer_key parameter's role in normalization and does not explain the trade-offs between different strategy/verifier options. Still, it is largely complete for the core functionality.

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

    Parameters3/5

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

    The schema covers 60% of parameters (request, n, answer_key have descriptions; strategy and verifier have enums but no descriptions). The description adds minimal parameter-specific meaning beyond the schema (e.g., 'samples best-of-N' relates to n, 'gates through verifier' relates to verifier). It does not significantly enhance understanding of the parameters.

    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: 'Buy a verified inference burst at a hard/irreversible/low-confidence decision.' It uses specific verbs ('buy', 'escalates', 'samples') and resource ('verified inference burst'), making it unambiguous. Although no siblings are provided, the description is sufficiently unique.

    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 explicitly advises when to use: 'Use when getting it wrong is costly.' This gives clear context. However, it does not mention when not to use or list alternatives, which would be ideal but is not required given the lack of sibling tools.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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