alphalabs_get_catalog
Free: list AlphaLabs Intelligence products, prices, and auth model.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
alphalabs-intelligence
TDQS
A3.7/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must carry the full burden. It only states what is listed, with no mention of side effects, idempotency, or safety. The 'Free' label is ambiguous regarding behavioral implications.
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?
One sentence, concise and front-loaded with 'Free'. Every word earns its place, though a more structured list of output contents could improve scannability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description is nearly complete for a simple listing tool. It lacks return format details, but for the use case it is adequate.
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?
With zero parameters, the baseline is 4. The description adds value by indicating the output contains products, prices, and auth model, which goes beyond the empty schema.
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 uses a specific verb 'list' and specifies the resource (AlphaLabs Intelligence products, prices, auth model). It clearly distinguishes from sibling tools which focus on calibration, evaluation, explanation, etc.
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 'Free:' prefix hints at zero cost or no authentication, but no explicit guidance on when to use versus alternatives. Sibling tool names provide implicit differentiation, but no direct usage conditions are stated.
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
A3.7/5.0
Disambiguation5/5
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.
Naming Consistency4/5
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.
Tool Count5/5
Six tools cover the core analytics and evaluation functionality without being excessive. Each tool serves a clear, necessary role in the workflow.
Completeness4/5
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.