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

With no annotations, the description carries the full disclosure burden and meets it: it explains the ranking semantics ('higher scores indicating higher predicted relative returns'), the top_n=0 default behavior, and enumerates the exact return payload including checksum and timestamps. This goes well beyond a mere 'getter' statement.

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?

The description is well organized into purpose, usage, parameter, and return sections. It is detailed but each sentence adds information; nothing is filler or repetitive.

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 single-optional-parameter read tool, the description covers when to use it, what the data represents, how ranking works, and the complete return schema. There is no obvious missing context an agent would need to call it correctly.

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 0%, but the description fully compensates by defining top_n's meaning, its default, the 'all instruments' behavior when 0, and the required non-negativity constraint. No parameter ambiguity remains.

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 names a specific verb ('Retrieve'), a specific resource ('latest quantitative stock selection signals produced by the Qlib ML pipeline'), and clarifies that scores are cross-sectionally ranked. It does not explicitly differentiate from sibling tools list_universe and signal_health, so it stops just short of a 5.

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?

The phrase 'Use this tool before making any trading or allocation decisions' gives a clear, explicit use case. However, it does not mention when not to use it or contrast it with signal_health/list_universe, so exclusions and alternatives are absent.

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

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.

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.

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.

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.

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.

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.

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

No annotations are provided, so the description carries the full behavioral burden. It discloses what the tool verifies—file existence, checksum validity, and acceptable age—and describes the returned health details. The read-only nature is implied by 'check' and 'verifies' rather than stated explicitly, so it is strong but not perfect.

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?

The description is well organized: lead purpose, usage context, verification details, parameter semantics, and return fields. It is somewhat verbose because it documents return fields even though an output schema exists, but every section adds useful context.

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 tool with one optional parameter and no annotations, the description is complete. It covers when to use the tool, what it verifies, the parameter meaning, and the full return payload, so an agent has everything needed to invoke it correctly.

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?

The input schema provides only a bare number with no description (0% coverage). The description fully compensates by defining max_age_hours as the maximum allowable signal age before data is considered stale and by noting the default of 24.0 hours.

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 opens with a specific verb and resource: checking the health, integrity, and staleness of the signals artifact. It then lists concrete checks (file existence, SHA-256 checksum, age) and positions the tool as a monitoring/sanity-check operation, which distinguishes it from the sibling tools that retrieve signals or list the universe.

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?

It explicitly states when to use the tool: for system monitoring and sanity checks before consuming signals in automated workflows. It does not name alternatives or exclusion conditions, but the intended use is clear enough to guide selection.

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