Data Coverage

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses rich operational behavior: sources queried sequentially due to a small per-key burst allowance, a failing source reported in notes rather than failing the call, no date ever guessed, book snapshots refreshed every 60 s with no history, and free-tier gating surfaced in notes.

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 purpose before the dataset inventory, and every clause carries information. It is dense and parenthetical-heavy, reading as one long paragraph, so it is effective rather than elegant, but nothing is padding.

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?

There is no output schema, so the description must describe returns, and it does: per-dataset window fields (with windowStart null + note when unavailable), freshness stamps, source notes, and the server { version, hiddenTools, toolCount } object. An agent has everything needed to call it and interpret the result.

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% (baseline 3), but the description goes further by glossing each opaque enum value — e.g. 'builder_ledger (fee ledger + attribution coverage)', 'census (chain-state stamp)', 'book (L4 order-book rollups ... no history)' — which the bare enum cannot convey. useToonFormat is left entirely to the schema.

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 states a precise verb+resource pair ('report the data window and freshness stamp of each dataset behind this server') and adds provenance (API route per figure). It is unmistakably a meta/diagnostic tool, clearly separable from the analytical siblings like trades, liquidations or cohort 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?

It gives an explicit trigger: 'check window and freshness before relying on a historical range or citing a date.' That is clear when-to-use guidance. It does not name a when-not condition or a competing sibling, which keeps it below a 5, though no true alternative tool exists in the roster.

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