alphalabs_get_catalog
Free: list AlphaLabs Intelligence products, prices, and auth model.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Free: list AlphaLabs Intelligence products, prices, and auth model.
| 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 the full burden. It discloses that the tool is 'Free' and lists the content types (products, prices, auth model). However, it does not mention rate limits, data freshness, or authentication requirements. Still, it adds meaningful context beyond the empty schema.
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?
Single sentence, front-loaded with key action and scope, no unnecessary words. Every word serves a purpose.
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 simple zero-parameter listing tool, the description is mostly adequate. It lacks details on output format or update frequency, but with no output schema and no annotations, it still provides sufficient context for an agent to decide to call it.
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?
Schema coverage is 100% (no parameters), so baseline 4. The description does not need to add parameter info since there are none.
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 it lists AlphaLabs Intelligence products, prices, and auth model. The verb 'list' and resource are specific, and the tool is distinct from sibling tools which focus on calibration, evaluation, and explanation.
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?
Usage is implied as a catalog listing, but no explicit guidance on when to use this versus siblings, nor any when-not conditions. Given the simplicity, a score of 3 is appropriate.
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.