Skip to main content
Glama
vigneshv1cky

AlphaDesk

by vigneshv1cky

baskets

Find stocks and funds that move together on the same news catalyst. Look up a group by symbol or name to discover likely co-movers and trigger correlation checks.

Instructions

AlphaDesk's baskets: groups of stocks and funds that move together on the same KIND OF NEWS, across industries — the bitcoin price, crypto rules, Fed and interest rates, the oil price, tariffs and China trade, chip export controls, AI spending, AI power demand, obesity drugs, healthcare policy, conflict and defense budgets, safe havens, consumer spending, travel demand, quantum computing, space, EV policy — plus a few named groups (Magnificent Seven, semiconductors). Each basket's why says which story moves it. Use it to find what else should move on a headline ("bitcoin jumped — what follows it?") or to start a correlation check. For groups by INDUSTRY use sector_performance. Membership is an editorial list in AlphaDesk's config, the same one the app's Baskets menu shows, plus any the reader made themselves (mine: true); nothing is scored, ranked or picked, and members are in the order written.

  • No arguments: every basket with its id, label and symbols.

  • symbol: only the baskets that contain it (its likely co-movers).

  • basket (an id or a label, any case): that one basket, each member with its company name; with quotes=true also each member's live quote on the reader's own vendor keys (one batched call).

To measure how closely members track each other, pull price_history for each and compare the returns yourself.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
basketNo
quotesNo
symbolNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses that membership is an editorial list, that no scoring or ranking is done, that order is as written, and that quotes use the reader's own vendor keys in a batched call. It also notes that nothing is scored or ranked, and for the no-argument case it returns every basket. This is transparent about non-obvious behavior, but does not detail the exact response format (though it mentions fields).

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?

The description is fairly long but front-loads the core concept and use cases, then lists parameters efficiently. It includes many examples (news types) which anchor the purpose, but could be slightly more concise without losing key guidance. Overall, each section earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (multiple modes, editorial source, quotes), the description covers all necessary aspects: what baskets are, when to use, how to use each parameter, limitations (editorial list, no scoring), and how to compute correlation. No output schema exists, but the description notes the fields returned for each mode adequately.

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?

Schema coverage is 0%, so the description must compensate, and it does well. It explains each parameter: no arguments returns all baskets, `symbol` filters by containing symbol, `basket` accepts id or label (any case) and optionally `quotes=true` to include live quotes. This adds meaning beyond the bare schema, giving clear behavioral definitions.

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 clearly states that baskets are groups of stocks that move together on specific kinds of news, with examples, and explains how to use it ('find what else should move on a headline' or 'start a correlation check'). It distinguishes from sector_performance, which is for industry groups. The verb 'list' is implied through the examples, but the resource and purpose are specific and actionable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states when to use this tool (find co-movers on news, start correlation checks) and when not to use it ('For groups by INDUSTRY use sector_performance'), giving an alternative. The description also provides guidance on measuring correlation via price_history. This is a clear when/when-not with a direct alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.