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

With no annotations, the description carries the full burden and does so unusually well: it discloses the frozen-input state since 2026-09-07, that prices are last-published rather than current, the usd_panel {frozen, as_of} response flag, the data provenance (real prices, 26.9M+ observations, 365-day history), and the degraded-mode behavior with its 'default_tcg_priors' label.

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 most consequential fact (frozen USD series) and the routing rule, which is good discipline. The seven-step math walkthrough and the 'NOT fake data' reassurance are longer than strictly needed for tool selection, but they earn their place given trust concerns around synthetic data.

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 4-parameter simulation tool with a full output schema and no annotations, the description supplies the missing pieces an agent needs: data freshness caveats, the alternative sibling, the fallback path, and response labeling. Return-value detail is correctly delegated to the output schema.

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 100%, so days, model, and simulations are already fully documented with ranges and defaults. The description explains the calibration pipeline but adds no syntax or behavioral detail about the parameters themselves, so the baseline 3 is appropriate.

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 ('Run a Monte Carlo price simulation for a trading card') and explicitly differentiates itself from the sibling get_forecast by naming the alternative's method (conformal VaR + Safe-Hold/Momentum grades) and its own (Merton/GBM stochastic paths). An agent can route between the two without opening either 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 explicit when-to-use guidance: use get_forecast for the 'honest DEFAULT forecast', use this tool when a stochastic Monte Carlo alternative is wanted, and notes it is 'opt-in'. It also names the exact conditions under which fallback priors apply (<5 data points).

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