QuantVibe

list_universe

Retrieve the complete list of stock tickers tracked by QuantVibe, including total count and effective date, to verify asset coverage before querying signals.

Instructions

List all stock tickers tracked by the quantitative research and signal generation pipeline.

Use this tool to inspect the active universe of assets covered by QuantVibe before querying
specific signals or checking market coverage.

Returns:
    A dictionary containing:
    - universe: Sorted list of ticker symbols (e.g. ['AAPL', 'MSFT', 'NVDA']).
    - count: Total number of instruments in the universe.
    - as_of: Effective date of the asset universe configuration (YYYY-MM-DD).

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 16, 2026

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries the behavioral burden; it clarifies this is a read-only listing operation and documents the exact return contract: universe, count, and as_of. It does not disclose edge-case behavior such as empty-universe handling or data freshness beyond the as_of field, but that is minor for a no-argument list tool.

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?

The description is compact, front-loaded with the primary action, and uses a short Returns section that adds concrete structure without bloat. Every sentence contributes to selection or invocation.

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?

For a no-argument, simple read tool, the description covers purpose, usage timing, and return semantics. Although an output schema is reportedly present, the description still provides enough detail for an agent to invoke and interpret the result without opening it.

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?

The tool has zero parameters, so there is nothing for the description to clarify. The baseline of 4 applies, and the description adds no irrelevant parameter noise.

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?

Opens with a specific verb and resource: 'List all stock tickers tracked by the quantitative research and signal generation pipeline.' It clearly scopes the tool to universe inventory rather than signal generation or health, distinguishing it from get_latest_signals and signal_health.

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

Usage Guidelines4/5

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

States when to use it: 'before querying specific signals or checking market coverage,' which positions it relative to the sibling signal tools. It does not explicitly name the alternative tools or state a when-not-to-use rule, so it stops short of a 5.

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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