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hub_whatsnew

See what changed since your last check: pulls journal activity tracked per agent, so you catch up on missed work without re-reading full status. New agents get a 24-hour window.

Instructions

Personalized "what did I miss" — journal activity since YOUR OWN last hub_whatsnew call (tracked per agent name), not a fixed time window like hub_brief. Call this at the start of a session/sweep instead of re-reading hub_status/hub_brief from scratch; a never-seen agent gets a 24h window on its first call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fullNoreturn everything, uncapped. By default long lists are trimmed to fit an agent context and what was left out is reported in `truncated`.
agentYesyour stable identity, e.g. "orchestrator" or your agent name — reused across calls to compute the delta
hoursNofallback window in hours if this agent has no prior checkpoint yet, default 24
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the stateful tracking mechanism ('tracked per agent name') and the first-call behavior ('never-seen agent gets a 24h window'), which implies the tool maintains a checkpoint. However, it does not explicitly state that the call updates the checkpoint or describe any side effects beyond that, so a small transparency gap remains.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, zero fluff. The first sentence front-loads the core purpose and differentiator; the second gives concrete usage guidance and an edge case. Every word earns its place, making it highly scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a stateful tool with no output schema, the description covers the essential context: what it does, when to use it, and the key behavioral nuance of per-agent checkpoints. It does not describe the return format, but the input schema's description of the `full` parameter mentions `truncated`, giving a hint. Given the tool's moderate complexity, this is nearly complete but not fully self-contained.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description references 'per agent name' (agent) and '24h window' (hours), but these details are already fully documented in the schema parameter descriptions. No additional parameter semantics are provided beyond what the schema already offers.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb+resource: 'journal activity since YOUR OWN last hub_whatsnew call'. It clearly differentiates from hub_brief by noting 'not a fixed time window like hub_brief', and from hub_status/hub_brief by recommending it as a replacement at session start. This is unambiguous and distinguishes among siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance is given: 'Call this at the start of a session/sweep instead of re-reading hub_status/hub_brief from scratch'. This tells the agent exactly when to use it and what not to use instead. The first-call fallback window is also explained, covering a key edge case.

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

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