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

Annotations (readOnlyHint true, idempotent, non-destructive) are consistent and the description adds substantial context beyond them: tickers absent on a date are silently dropped rather than erroring, forward return is computed from raw unadjusted close, grids over 120 cells return a stream_fallback with signed Parquet URLs instead of truncating, and free-tier callers hit an ENTITLEMENT_DENIED envelope before any read. That is exactly the auth/limit/degradation detail annotations cannot carry.

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 critical constraint ('SMALL, BOUNDED ... NOT a full-universe backtesting engine') is front-loaded in the first clause. It is longer than needed, though: the not-a-real-backtest caveat and the 'small complement' framing are restated in three separate places (opening, middle, closing), which costs tokens without adding decision-relevant information.

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 composed, tier-gated analytical tool the description covers the full decision surface: inputs, output grid shape, correlation output, overflow behavior, entitlement failure mode, and the correct alternative for heavier workloads. An output schema exists, yet the description still sketches the return shape, which is a bonus rather than a gap.

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 100%, so the baseline is 3. The description still adds interpretive value: the 10x12=120 cell budget frames how to choose tickers vs dates, the factors list maps to get_pit_valuation_ratios' own output fields with an all-included default, and the delisted/not-yet-listed drop rule tells the agent what an unmatched ticker on a date actually yields.

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?

States a specific verb+resource with explicit scope: a bounded in-Worker factor sanity-check over up to 10 tickers x 12 rebalance dates. It names exactly which sibling tools it composes (get_pit_universe + get_pit_valuation_ratios) and explicitly contrasts itself with a full-universe backtesting engine, so an agent can distinguish it from get_valuation_metrics or the SDK path 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.

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

Gives an explicit when-to-use scenario ('does this factor actually work on these 5 names over the last year' inline, mid-conversation) and an explicit when-not-to-use plus alternative (real full-universe survivorship-free backtest → Python SDK AlphaEngine looped over as_of dates). It also states the escalation path when the grid overflows.

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