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Delimit Vendor News Scan

delimit_vendor_news_scan

Scan watchlisted vendor accounts for updates and auto-draft riffs, enabling on-demand execution of the vendor-news sensor.

Instructions

Scan watchlisted vendor accounts and auto-draft riffs (Pro) (LED-1253).

When to use: for ad-hoc execution of the vendor-news sensor (the cron is the normal autonomous path). When NOT to use: for a single tweet (use delimit_vendor_news_draft) or subsystem health (delimit_vendor_news_health).

Sibling contrast: delimit_vendor_news_draft is one tweet; delimit_vendor_news_health is health rollup; this is the full sensor + drafter pass.

Side effects: gated by require_premium. Wraps ai.vendor_news.sensor.scan_vendor_news + draft_vendor_riff. dry_run=True polls (cache-friendly) but skips JSONL log write AND skips the drafter entirely (no queue, no rate-cap consumption).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dry_runNoIf True, sensor-only (no drafter, no queue). Default False.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Addedv4.7.9

TDQS

A5/5.0
Behavior5/5

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

The description goes well beyond the annotations by detailing side effects: require_premium gating, wrapped function calls, JSONL log writes, queue behavior, and rate-cap consumption. It also explains exactly what dry_run does and does not do, which is valuable behavioral context not present in the structured annotations.

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?

The description is organized with clear labeled sections: purpose, when to use, when not to use, sibling contrast, and side effects. Every sentence contributes useful decision-making information, and the most critical scoping information is front-loaded.

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?

For a tool with one optional parameter, explicit sibling differentiation, detailed side-effect disclosure, and an output schema available, this description is complete. There are no obvious gaps an agent would need to resolve elsewhere before invoking the tool.

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?

Although schema coverage is 100%, the description adds meaning beyond the dry_run schema description by clarifying that dry_run=True is cache-friendly, skips the JSONL log write, and skips the drafter entirely with no queue or rate-cap consumption. This materially improves an agent's ability to choose the right value.

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 opens with a specific verb and resource ('Scan watchlisted vendor accounts and auto-draft riffs') and clearly distinguishes this tool from its closest siblings by naming exactly what each sibling does. This makes the tool's identity unambiguous even among a large family of delimit_ tools.

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?

The description provides an explicit 'When to use' section, a 'When NOT to use' section, and names the alternative tools for each exclusion case. An agent can confidently decide between scan, draft, and health without needing to inspect their schemas.

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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