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defog-ai

analysis-gym

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by defog-ai
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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clear, distinct purpose: recording predictions, recording actuals, and fetching results. There is no overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (get_results, record_actuals, record_prediction), making them predictable and easy to understand.

    Tool Count5/5

    With 3 tools, the server is tightly scoped to the core operations of the earnings analysis domain—recording predictions, recording actuals, and retrieving results. No unnecessary tools.

    Completeness5/5

    The tool surface covers the full lifecycle of the domain: creating predictions, recording actuals to settle them, and retrieving results with performance metrics. No obvious gaps.

  • Average 3.5/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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.

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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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It discloses that the tool modifies data ('settle earlier predictions') and defines key metrics. Missing are side effects like overwrite behavior, triggers, or access requirements.

    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 short and front-loaded with the core action. The definitions are clear and relevant. Minor improvement: could group parameter explanations more tightly.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Despite an output schema existing, the description fails to explain parameter formats or constraints, leaving many parameters unclear. Sibling tools indicate a workflow, but integration guidance is missing. The description is incomplete for a tool with 10 parameters.

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

    Parameters1/5

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

    Schema coverage is 0%—the description does not document any of the 10 parameters. It defines related terms (EBITDA, free cash flow) but ignores parameters like earnings_at, fiscal_period, and currency, leaving the agent without critical guidance.

    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: 'Record published actuals and settle earlier predictions for the event.' The verb 'record' and resource 'actuals' are specific. Sibling tools likely include a prediction variant, providing implicit differentiation.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides definitions for EBITDA and free cash flow, and explains which close price to use based on release time. However, it does not explicitly state when to use this tool vs. alternatives (e.g., record_prediction) or when not to use it.

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

  • Behavior3/5

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

    With no annotations, description explains output content and SMAPE meaning, but does not cover edge cases, pagination, or auth requirements. Adequate but not thorough.

    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?

    Two focused sentences with no filler. First sentence states main output, second adds key insight.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given 6 parameters and output schema, description covers main output but omits parameter semantics for most fields, leaving some gaps for agent usage.

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

    Parameters2/5

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

    Only settled_only is explained in description; 0% schema coverage leaves 5 parameters unexplained. Fails to compensate for lack of schema 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?

    Clearly states verb (return) and resources (predictions, per-metric errors, leaderboard), distinguishing from sibling tools which record data.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Implicitly suggests usage for retrieving results vs recording siblings, but lacks explicit when-to-use or alternatives. Provides parameter guidance for settled_only.

    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, the description carries full disclosure burden. It highlights immutability ('immutable pre-earnings forecast') and format constraints. However, it does not mention idempotency, error behavior, side effects, or rate limits. Sufficient but not exhaustive.

    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 three sentences, front-loaded with the core purpose. Every sentence adds necessary constraints or definitions. No wasted words.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given 13 parameters, 0% schema description coverage, and an output schema that is not described, the description leaves many gaps. It explains only a subset of parameters and does not cover return values, error conditions, or prerequisite checks for a complex recording tool.

    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 0%, so description must compensate. It explains semantics for expected_earnings_at (ISO 8601 with timezone), financial fields (same currency), and post_earnings_close. However, 8 of 13 parameters (e.g., ticker, fiscal_period, harness, model, thinking_setting, notes, currency) remain unexplained. Adds value for some but not all.

    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 'Record one immutable pre-earnings forecast.' This is a specific verb (record) and resource (immutable pre-earnings forecast). It distinguishes from siblings 'get_results' (reads) and 'record_actuals' (records actuals), so an AI agent can tell when to use this tool.

    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 provides explicit constraints: ISO 8601 timestamp with timezone, same reporting currency for financial fields, and definition of post_earnings_close. It implies use for pre-earnings forecasts but does not explicitly state when not to use or compare to siblings. Still clear context.

    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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