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Glama

RiskModels Multi-Ticker Comparison

riskmodels_compare
Read-only

Compare market, sector, subsector, and residual risk for 2-100 stocks side by side, with each ticker's risk shares and hedge ratios.

Instructions

Use when you want 2-100 stocks side by side on market, sector, subsector and residual risk. Not for one stock (use riskmodels_decompose) or a weighted portfolio (use riskmodels_analyze_portfolio). Returns, from POST /batch/analyze, each ticker's explained-risk shares and hedge ratios by layer, with chart-ready data. End-of-day data, refreshed after each US market close. Price: $0.015 per ticker, minimum $0.03 per call.

Input Schema

NameRequiredDescriptionDefault
tickersYes2-100 US stock ticker symbols, e.g. ["AAPL","NVDA"]

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.0.12
    • changedInput schema / properties / tickers / description
      Previous value: -"Ticker symbols to compare"New value: +"2-100 US stock ticker symbols, e.g. [\"AAPL\",\"NVDA\"]"
  2. First observedv1.0.11

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false), and the description goes well beyond them: the upstream endpoint (POST /batch/analyze), the exact return content (explained-risk shares and hedge ratios by layer, chart-ready), data freshness (end-of-day, refreshed after each US market close), and billing terms ($0.015 per ticker, $0.03 minimum). This is unusually rich behavioral context.

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?

Four tight sentences, front-loaded with usage and exclusions, then returns, then freshness and cost. Every sentence carries distinct information an agent needs; no filler.

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?

There is no output schema, so the description must carry return-value disclosure, and it does: risk-share and hedge-ratio outputs by layer with chart-ready framing. Combined with cost, freshness, and routing guidance, an agent has everything needed to call and interpret this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With a single parameter at 100% schema description coverage, the schema already documents the ticker array including the 2-100 bounds. The description's '2-100 stocks' and ticker-symbol framing restate the schema rather than adding format, ordering, or validation nuance. Baseline 3 is appropriate.

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?

States a specific operation (side-by-side comparison) on a specific resource (2-100 stocks across market, sector, subsector and residual risk layers). It explicitly names the siblings it is not (riskmodels_decompose for one stock, riskmodels_analyze_portfolio for a weighted portfolio), so an agent can select it without opening any schema.

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?

Opens with an explicit when-to-use condition (2-100 stocks side by side) and follows with two explicit when-not conditions paired with the correct alternative tools. Nothing about tool selection is left to inference.

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