alphalabs_feature_attribution
Which engine inputs actually predict outcomes, measured on recorded live results: Spearman rankings, median-split deltas, dead inputs.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
AlphaLabs Intelligence
TDQS
B3.2/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose behavioral traits (e.g., read-only, idempotent, destructive). No mention of side effects, auth requirements, or performance implications.
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?
Single sentence, no wasted words. Front-loads the core question and lists key outputs concisely.
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?
Describes the tool's purpose and outputs adequately but lacks details on return format or behavior edge cases. For a tool with no parameters and no output schema, it is somewhat complete but could be clearer about what 'dead inputs' means.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so schema coverage is trivially 100%. The description adds meaning by explaining the tool's output (Spearman rankings, etc.), though it does not describe param details since none are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool measures feature attribution on live results using specific metrics (Spearman rankings, median-split deltas, dead inputs). It distinguishes from sibling tools by mentioning these unique outputs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like alphalabs_calibration_report or alphalabs_explain_decision. No prerequisites or exclusions mentioned.
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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TDQS
A4/5.0
Disambiguation5/5
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.
Naming Consistency4/5
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.
Tool Count5/5
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.
Completeness5/5
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.