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

Annotations already establish readOnly/idempotent/closed-world, so the safety bar is low, and the description adds meaningful behavior beyond them: it discloses the exact output content (counts, median, range, number of cycles), the data provenance caveat (no open crypto price history since 1990), and mandatory framing rules (describe as history, never as forecast/signal/advice). This is rich behavioral context not derivable from annotations.

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 core content is front-loaded ('what markets price now... and what happened over the next 12 months'), but the single paragraph runs long and packs an enumerated data-category list plus multiple compliance instructions into one sentence chain. Much of the framing/phrasing mandate is useful, yet the density and repetition of the coverage list dilute scanability.

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?

With no input parameters and no output schema, the description carries the full disclosure burden and discharges it: it explains the return values (report counts, median, range, cycle count), the data scope and gaps, the required attribution, and how to phrase results. An agent has everything needed to invoke it and present results 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 tool takes zero parameters, so the baseline is 4. It nonetheless clarifies the implicit fixed configuration by noting the analog selection reuses the 'same 14 sensors as find_similar_days' and requires periods 'at least half a year apart,' which helps the agent understand what inputs drive the result even though nothing is user-supplied.

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 deliverable: current market-implied prices (Fed path from 2y yield, breakevens, yields) plus the historical 12-month outcomes following the 10 most similar days since 1990. It explicitly distinguishes itself by tying its selection method to the sibling find_similar_days ('same 14 sensors'), so an agent can separate it from get_current_readings and get_monthly_history without opening schemas.

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 strong context on what the tool returns and references find_similar_days as the underlying selection basis, implying when it is relevant (comparing today to historical analogs). However, it never states when to prefer this over get_current_readings, get_changes_since, or what_to_watch, and offers no explicit exclusions. Usage is inferable but not prescribed.

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