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create_scout

Set up a monitoring scout to continuously track web changes based on your query and receive alerts.

Instructions

Create a monitoring scout for continuous web monitoring. Scouts track changes relevant to a query and alert you. Examples: 'news about Yutori', 'H100 pricing below $1.50'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language description of what to monitor. Examples: 'Tell me about the latest news, product updates, or announcements about Yutori', 'when H100 pricing per hour drops below $1.50', 'anytime a startup in SF announces seed funding'
output_intervalNoSeconds between scout runs. Minimum 1800 (30 minutes). Default: 86400 (daily)
webhook_urlNoHTTPS URL to receive webhook notifications when updates are available. Must use https://. Confirm the URL with the user before setting.
webhook_formatNoWebhook payload format: 'scout' (default), 'slack', or 'zapier'
output_fieldsNoOptional: Extract structured data as an array of objects with these field names. Example: ['headline', 'summary', 'url']. If omitted, returns human-readable text. For complex schemas, call the Yutori REST API directly (see example at: https://docs.yutori.com/reference/scouts-create#using-scheduling-webhooks-and-a-structured-output-schema).
user_timezoneNoTimezone for scheduling. Example: 'America/New_York'. Default: 'America/Los_Angeles'
skip_emailNoIf true, skip email notifications (useful with webhooks)
start_timestampNoUnix timestamp for when monitoring should start (0 = immediately)
user_locationNoUser location for geo-relevant searches. Format: 'city, region, country'
is_publicNoWhether scout results are publicly accessible
Behavior3/5

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

No annotations provided, so description bears full burden. It discloses that scouts track changes and alert, but lacks details on mutation, auth requirements, or potential costs.

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 plus examples, no wasted words. Every part earns its place.

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

Completeness3/5

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

For a tool with 10 parameters and no output schema, the description is brief. It covers core purpose and examples, but omits guidance on optional parameters like webhook, timezone, or output_fields.

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 schema documents all parameters well. The description adds query examples but no additional meaning beyond what the input schema already provides.

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 action 'Create a monitoring scout' and the purpose 'continuous web monitoring', with two concrete examples that distinguish it from sibling tools like delete_scout, edit_scout, etc.

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?

The description implicitly contrasts with one-off tasks by emphasizing 'continuous' monitoring, but does not explicitly mention when to use this vs. alternatives like run_browsing_task or run_research_task.

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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