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

With no annotations, the description carries the disclosure burden, and it does communicate that this is a passive read returning row counts and ingest timestamps. However, it says nothing about scope (all tables vs. per-table breakdown), whether missing tables are reported, or any auth/rate considerations.

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?

Two short sentences: the first defines the payload, the second defines when to call it. The most decision-relevant information (what you get, when to use it) is front-loaded with no filler.

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 zero-parameter tool with no output schema, the description must convey what comes back, and it does by naming row counts and last ingest time. Minor gaps remain around granularity (per-table vs. aggregate) and behavior when tables are empty or missing.

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 takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description correctly avoids implying any filtering options exist.

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 resource ('hl_research_* tables') and specific outputs ('row counts and last ingest time'), with the table families enumerated (candles, funding, symbols). It is clearly a data-quality/stats read rather than a backtest or market-listing tool, though it does not name the sibling backtest tools directly.

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 gives an explicit when-to-use ('Use before backtests to confirm data freshness'), which is actionable context. It stops short of stating any exclusions or naming run_hyperliquid_research_backtest as the alternative, so the routing is implied rather than spelled out.

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