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 provided, so the description carries full burden. It clearly states the tool is read-only and provides derived analytics, not raw positions or orders. This fully discloses the non-destructive, analytical nature of the tool.
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?
Two concise sentences, front-loaded with purpose. Every word adds value: first sentence defines the telemetry, second clarifies boundaries. No waste.
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?
This is a simple tool with no parameters or output schema. The description explains the data it provides (stage funnel, gate failures, near-misses) and explicitly states what is excluded, making it complete for its intended use.
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 no parameters and schema coverage is 100% (empty schema). Baseline per instructions is 4 for 0 parameters. The description adds no parameter-specific information, which is acceptable given no parameters exist.
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 explicitly states the tool provides 'calibration telemetry: stage funnel, gate failures, near-misses' from a 'live paper-trading pipeline'. It also clarifies what it does not contain ('no positions, orders, or account data'), clearly distinguishing it from sibling tools like alphalabs_outcome_report or alphalabs_get_catalog.
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 implies the tool is for calibration monitoring and explicitly excludes trading data, guiding use. However, it does not explicitly state when to prefer this over siblings or provide alternative tool names. The context is clear enough but lacks explicit 'when not to use' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: calibration telemetry, signal evaluation, explanation, feature attribution, catalog listing, and outcome reporting. No two tools overlap in functionality.
All tools share the 'alphalabs_' prefix and follow a verb_noun or noun_noun pattern. The mix of verb_noun (evaluate_signal, explain_decision, get_catalog) and noun_noun (calibration_report, feature_attribution, outcome_report) is a minor inconsistency, but still predictable and readable.
With 6 tools, the server is neither too sparse nor too heavy. Each tool covers a distinct aspect of the intelligence pipeline, making the set well-scoped for its purpose.
The tool surface covers the full lifecycle of signal evaluation: evaluation, explanation, feature analysis, calibration reporting, and outcome tracking. Missing features like market data access are explicitly out of scope, so no dead ends remain.