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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct and non-overlapping purposes: oracle_validate is for submitting actions for human approval, while oracle_poll_status is for checking the status of those submissions. Their descriptions clearly differentiate them, with no ambiguity in functionality.

    Naming Consistency5/5

    Both tools follow a consistent 'oracle_verb_noun' pattern (oracle_poll_status and oracle_validate), using snake_case and starting with the server prefix. This makes them predictable and easy to identify within the server's namespace.

    Tool Count2/5

    With only two tools, the server feels thin for its apparent purpose of handling human approval workflows. While the tools cover the core submit-and-poll cycle, there are likely missing operations like listing pending requests or managing notifications, making the scope underdeveloped.

    Completeness3/5

    The server provides basic coverage for the human approval domain with submit and poll operations, but there are notable gaps. Missing tools for operations like canceling requests, viewing request history, or configuring approval settings limit its completeness, though agents can still work with the core workflow.

  • Average 4.4/5 across 2 of 2 tools scored.

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

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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 the polling behavior, timing recommendations, and status-based outcomes ('approved', 'rejected', 'expired'). However, it doesn't mention error handling, rate limits, or authentication requirements, leaving some behavioral aspects unspecified.

    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 extremely efficient with three sentences that each serve distinct purposes: stating the core function, providing polling guidance, and specifying expiration handling. There is zero wasted text, and the most critical information appears first.

    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?

    For a single-parameter polling tool with no output schema, the description provides excellent context about the polling behavior, timing, and status outcomes. However, it doesn't describe the return format or what data the status check actually provides beyond the three mentioned states, which leaves some uncertainty about the tool's complete behavior.

    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?

    Schema description coverage is 100%, so the schema already documents the request_id parameter. The description adds context by mentioning this is 'returned by oracle_validate', which provides useful semantic linkage but doesn't add significant technical details beyond what the schema provides. This meets the baseline for high schema coverage.

    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 specific action ('Check the current status') and resource ('a validation request previously submitted via oracle_validate'), distinguishing it from its sibling oracle_validate. It explicitly identifies what the tool does and what it operates on.

    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 instructions: 'Poll every 10-15 seconds until status is "approved" or "rejected"' and 'If "expired", the human did not respond in time — abort the action.' It gives clear timing guidance and specific conditions for when to stop polling or abort, which is comprehensive guidance for when and how to use this tool.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • 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 tool triggers human approval via Telegram notification, the action is critical/irreversible, and it returns a request_id for polling. However, it doesn't mention rate limits, authentication requirements, or error handling scenarios.

    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 perfectly structured and concise with zero waste. Three sentences each serve distinct purposes: stating the tool's purpose, providing usage guidelines, and explaining the return value. Every sentence earns its place and the information is front-loaded appropriately.

    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 (human-in-the-loop approval system) and lack of annotations/output schema, the description provides good contextual completeness. It explains the approval workflow, Telegram notification mechanism, and relationship with oracle_poll_status. However, it doesn't describe what happens after timeout or error scenarios, leaving some gaps.

    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?

    Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description doesn't add any additional parameter semantics beyond what's in the schema. The baseline score of 3 is appropriate when the schema does the heavy lifting for parameter documentation.

    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 specific verbs ('submit a critical or irreversible action to ORACLE-H for human approval') and distinguishes it from its sibling oracle_poll_status by explaining the relationship. It precisely defines what the tool does: submitting actions for human approval via Telegram notification.

    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: 'ALWAYS use this tool before executing any destructive, financial, or irreversible operation.' It clearly defines when to use it (for critical/irreversible actions) and references the alternative tool oracle_poll_status for retrieving decisions, creating a complete workflow.

    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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