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

With no annotations, the description carries the transparency burden. It discloses the core behavior: returns a mean over members present on each date, includes members_scored, and warns that membership changes over time can confound interpretation. This goes well beyond a generic 'returns history' statement, even if operational details like default time range and result shape are omitted.

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 description is well-structured and front-loaded, but the origin-story sentence about per-provider and whole-dataset series adds length. It is informative, yet a tighter version would keep the same value with less narrative.

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?

The description explains the return semantics well, including the mean calculation and members_scored field, but it lacks explicit parameter guidance, time-range controls, or response format. For a tool with no output schema and no annotations, this leaves an agent needing to infer too much.

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 only 33%, so the description must compensate for undocumented kind and slug parameters. It does not explicitly explain what kind or slug mean, how they relate, or what slug values look like. The 'banking' example only indirectly hints that slug identifies a market, which is insufficient for reliable parameter construction.

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 clearly identifies this as a set-level time series showing how a market's Kin Score and agent readiness moved, and states that it returns the mean over members present plus members_scored. It distinguishes the tool from per-provider and whole-dataset series, though it does not name specific sibling tools.

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?

The description gives a concrete use case ('how has banking moved over six months') and positions the tool as filling the gap between per-provider and whole-dataset views. This provides clear context for when to use it, though it stops short of explicitly naming alternatives or when-not-to-use conditions.

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