Codex Reset Monitor

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

With no annotations, the description carries the full disclosure burden. It does add real value by distinguishing 'verified' resets from 'sighting' signals and naming the data source (public monitoring signals maintained by Agenai), which tells the agent about provenance and reliability tiers. It omits freshness/latency and what is returned when no reset has been recorded, so the behavioral picture is incomplete.

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?

Two tight sentences, front-loaded with the primary payload and followed by provenance. Terms like 'banked-reset sighting' are domain jargon left unexplained, which slightly reduces immediate clarity.

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 zero-parameter read tool with no output schema, the description does the necessary work by enumerating what comes back (latest verified reset, latest sighting, totals, coverage) and where the data originates. It does not describe the shape of the response or edge cases such as an absent latest reset, which is the remaining gap.

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?

There are zero parameters, so the baseline is 4; the schema has nothing for the description to explain. The description correctly signals that no filtering or date input is needed.

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 resource and scope: the single most recent verified Codex global reset plus the latest banked-reset sighting, along with totals and coverage. An agent can tell it returns the newest record rather than a listing, which implicitly separates it from list_reset_events, but it never names the siblings so the differentiation is inferential.

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 is only implied by 'most recent' — an agent must reason that this is for a current snapshot versus get_reset_stats (aggregates) or list_reset_events (history). There is no explicit when-to-use, when-not-to-use, or alternative routing statement.

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

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

With no annotations, the description carries the full burden, and it does disclose meaningful behavior: the scope is the entire recorded history with no filtering, timestamps are normalized to Asia/Shanghai, and the tool explicitly does not predict. It omits any note on cost, caching, or data freshness, which is the remaining gap for a read-only aggregate.

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?

A single dense sentence that front-loads the scope ('full recorded history'), lists the returned metrics compactly, and closes with the key constraint ('History only — not a prediction'). No filler and nothing buried.

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 parameterless, annotation-free tool with no output schema, the description must explain the return shape — and it does so by naming every computed metric. Combined with the scope and timezone disclosure, an agent has everything needed to call it and interpret results.

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. The description correctly communicates that no filtering is possible by emphasizing 'over the full recorded history', which prevents an agent from hunting for a date-range argument that does not exist.

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 uses a specific verb+resource ('Cadence statistics over the full recorded history') and enumerates the exact metrics returned: total verified resets, 30/90-day counts, gap statistics (avg/median/min/max), monthly counts, and announcement-time distribution. This clearly differentiates it as the aggregate-statistics endpoint, though it never names the siblings (get_latest_reset, list_reset_events) that an agent would choose between.

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 is implied rather than stated: 'History only — not a prediction' is an exclusion that tells the agent this is not a forecasting tool, but there is no explicit guidance on when to pick it over get_latest_reset or list_reset_events. The agent must infer that aggregate cadence questions belong here while single-event questions belong to the siblings.

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

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

With no annotations, the description carries the full burden. It usefully discloses result ordering (newest first) and defines what each event type actually means, which is real domain behavior. It does not state that the tool is read-only, whether results are paginated or truncated by limit, or any auth/rate-limit constraints.

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?

Two compact sentences with the core purpose front-loaded and no filler. The enum glossary is telegraphic ("type 'reset' = ..."), which is efficient but slightly abrupt as prose.

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 simple two-parameter read tool with full schema coverage and no output schema, the description covers purpose, ordering, and filter semantics well. The main remaining gap is that it never says the default is 'all' or how limit interacts with the result set, though the schema covers the defaults.

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%, so the baseline would be 3, but the description goes beyond the schema's generic "Event type filter" by defining each enum value: 'reset' as verified global resets, 'credits' as banked-reset sightings, 'all' as both. That distinction is not derivable from the enum names alone. The limit parameter is left entirely to the schema, which documents it adequately.

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?

"List recorded Codex events, newest first" gives a specific verb (list) and resource (Codex events) plus a result ordering. It does not name the sibling tools (get_latest_reset, get_reset_stats), so the agent must infer the boundary from the plural/collection framing rather than being told explicitly.

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 is implied: this is the collection-listing tool, so it is the right choice when the agent wants many events rather than the single latest one. There is no explicit when-to-use/when-not statement and no mention of the siblings that would make the choice unambiguous.

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