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

With no annotations, the description carries the full burden and does well: it discloses the return shape (sample size, mean/median returns, positive outcome rate, methodology, provenance, limitations) and clearly flags research limitations rather than overselling results. It does not state cost, latency, or whether results are cached, and read-only safety is only implied by the verb 'Evaluate' and 'Returns structured JSON' rather than declared.

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?

Front-loaded with the core capability and tightly written for the amount of scope it must convey. The middle clause enumerating return fields is partly redundant given that an output schema exists, which is the main economy miss; the exclusion sentences are well spent because no annotations exist to convey safety and scope.

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 two-parameter, read-only research lookup with an output schema present, the description supplies everything an agent needs: what it computes, the single supported study, valid horizon values, what comes back, and what the results may not be used to claim. Nothing required for correct invocation or correct interpretation is 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?

Schema description coverage is 0%, so this text must carry parameter meaning, and it largely does: it pins study_id to the single valid value 'pead_nordic_pre_report_drift_20d_q5' and constrains horizon_days to '20 or 40 trading days', effectively acting as enums. It stops short of naming the parameters explicitly or saying what happens if an unsupported study or horizon is supplied, so it is strong but not exhaustive.

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-resource pair ('Evaluate historical evidence for a supported equity hypothesis') and then enumerates exactly what the agent does NOT have to do (collect prices, reconstruct earnings events, run a backtest, compute statistics). It further names the single supported study, so the scope of the answer is unambiguous before any schema is opened.

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?

Usage boundaries are explicit and appear in both directions: the negative list in the first sentence tells the agent this replaces manual backtesting work, and the closing line ('Precomputed event-level research only: not an arbitrary backtest, live trading signal, or proof of investable portfolio performance') states the cases where the tool must not be relied on. No alternatives exist among siblings, and none are needed.

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