Live paper-trading pipeline calibration telemetry: stage funnel, gate failures, near-misses. Derived analytics only — no positions, orders, or account data exist on this surface.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full burden. It correctly labels the tool as returning derived analytics only, implying a read-only behavior. However, it does not disclose any other behavioral traits such as response time, pagination, or rate limits. The description is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single well-structured sentence that front-loads the key purpose and includes a clarifying exclusion. Every part adds value with no redundancy or wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there are no parameters and no output schema, the description fully explains what the tool returns (derived analytics) and what it does not (positions, orders, account data). This is complete for a reporting tool with no inputs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and no input schema constraints (100% schema coverage). The description adds significant meaning by specifying the content of the report (stage funnel, gate failures, near-misses) and clarifying what it does not contain (positions, orders, account data). This compensates for the lack of parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides live paper-trading pipeline calibration telemetry including stage funnel, gate failures, and near-misses. It emphasizes that it is derived analytics only, differentiating it from siblings that might involve positions, orders, or account data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context: it is for calibration telemetry and explicitly states what it does not cover (positions, orders, account data), guiding users away from using it for those purposes. However, it does not name specific alternative tools, leaving some inference required.
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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Each tool targets a distinct aspect: pipeline telemetry, signal evaluation, decision explanation, feature attribution, product catalog, and aggregated outcome reports. No two tools overlap in purpose.
All tools share the 'alphalabs_' prefix and are descriptive. However, some are verb-noun (evaluate_signal, explain_decision, get_catalog) while others are noun-noun (calibration_report, feature_attribution, outcome_report), creating a minor inconsistency.
Six tools cover the core analytics and evaluation functionality without being excessive. Each tool serves a clear, necessary role in the workflow.
The set covers signal evaluation, explanation, attribution, and aggregated reports. Missing a tool to list prior evaluations or manage them, but the core analytical surface is well covered.