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create_watch

Register a standing watch on a freight lane to get server-side alerts when specified thresholds—spot rates, reliability, disruptions, forecast moves, or book-now windows—are triggered.

Instructions

Register a standing WATCH on a lane so freight-pulse alerts you when something changes — the feature that makes it a recurring daily tool, not a one-shot lookup. Give a lane + one or more THRESHOLDS and it persists the watch SERVER-SIDE (in our own store, per your key): 'spot-below' / 'spot-above' a USD level (buying window / cost ceiling), 'reliability-below' a score (rollover/delay risk), 'disruption' (any active disruption appears on the corridor/ports), 'book-now' (the forecast/timing engine flips to a book-now window), or 'forecast-rising' / 'forecast-falling' beyond a % move (act early / wait). It returns the watch id and the registered thresholds. Pair it with check_watches, which your agent polls (e.g. daily) to get back only the alerts that fired. Honest (regla 7): evaluated against modeled engine outputs — an alert is a signal to look, not a booking trigger. PREMIUM: pay per call with x402 (USDC on Base) or a prepaid key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
origin_portYesOrigin port to watch.
dest_portYesDestination port to watch.
container_typeNoContainer '20ft'/'40ft'/'40HC'. Optional; default '40ft'.
thresholdsYesConditions to fire on. Each: { metric, value? }. metric ∈ spot-below | spot-above | reliability-below | disruption | book-now | forecast-rising | forecast-falling. value: USD for spot-*, 0-100 for reliability-below, % for forecast-*. e.g. [{ metric:'spot-below', value:2800 }, { metric:'disruption' }].
labelNoOptional name for the watch.
Behavior5/5

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

With no annotations, the description carries full burden and discloses server-side persistence, return values, alert nature (signal, not trigger), and premium pricing thoroughly.

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

Conciseness4/5

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

The description is front-loaded with purpose and each sentence adds value, though slightly verbose. Still efficient overall.

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

Completeness5/5

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

Given the complexity (5 params, no output schema, no annotations), the description is complete: covers purpose, usage, parameters, behavior, and integration with sibling.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds significant value by explaining each threshold metric in detail and providing examples, far beyond the schema.

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 clearly states the tool registers a standing watch on a lane for freight-pulse alerts, using specific verbs and distinguishing it from siblings like check_watches.

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

Usage Guidelines4/5

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

It explains when to use (for recurring alerts) and how to pair with check_watches for polling. While it doesn't explicitly say when not to use, the context is clear.

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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