Recorded outcomes of the live pipeline's own decisions: hit rates, score-band tables, accepted-vs-rejected edge, gate near-miss regret. Aggregated engine telemetry — percent moves and counts only.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the tool returns aggregated engine telemetry (percent moves and counts only) and implies read-only access. However, it does not address staleness, caching, or any constraints beyond content.
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 sentences with no redundancy. The first sentence lists key content; the second clarifies scope (aggregated telemetry, percent moves and counts). Every sentence adds value.
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?
For a parameterless report with no output schema, the description adequately describes the data nature. It could specify time context or filtering options, but the current level is sufficient for understanding what the tool returns.
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?
No parameters exist in the input schema, so the baseline is 4. The description adds no parameter information, which is appropriate as none are needed.
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 specifies the tool provides recorded outcomes of the live pipeline's decisions, including hit rates, score-band tables, and edge metrics. It distinguishes from siblings by focusing on aggregated engine telemetry rather than calibration or explanations.
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?
No explicit guidance on when to use this tool versus alternatives like alphalabs_calibration_report or alphalabs_explain_decision. The description only states what the tool contains, not the context for its use.
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.