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Run a lane now

gtm_run_source
Destructive

'Run once now' — run ONE lane immediately through the same code the sourcing tick runs, with force: an OFF lane still runs (that is what run-once means while configuring), a DRAFT play still runs (how you try a lane before launch), a PAUSED play does not. REAL: provider calls are made, leads are inserted, the play's daily budget and the org's monthly lead quota are spent. Only call it when the human asked for a run in this conversation; to look without spending, use gtm_preview_source. Returns the run's funnel (scanned / dropped / droppedIntent / added / duplicates / capped), the unmapped ICP facets, and skipped + reason when it could not run (no_seat, needs_config, icp_empty, daily_cap, lead_quota, no_results, provider_error).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesWhich lane. people_search: LinkedIn people search compiled from the play's ICP (+ optional free-text keywords). | competitor_posts: Post discovery: Otto searches LinkedIn daily for posts matching an intent, judges each, and takes who engaged. | job_offers: Hiring signal: companies posting a role → the buyers inside them. | account_list: Companies you name (names, websites or LinkedIn page URLs) → Otto finds your buyers inside each. | post_engagers: RETIRED — folded into competitor_posts.postUrls. Still runs on rows that exist; do not configure it on new plays.
playIdYesThe play, from gtm_list_plays.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The annotations already flag destructive/world-changing behavior, but the description goes much further: provider calls are made, leads are inserted, budgets and quotas are consumed, OFF and DRAFT plays still run while PAUSED does not, and it enumerates skipped reasons. This fully discloses the operational impact beyond the annotations. No contradiction with annotations found.

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 dense but orderly: a one-line summary, force behavior, real-world effects, call condition, alternative, and return shape. Every sentence earns its place, and the most important safety and differentiation 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 destructive tool with no output schema, the description is essentially complete: it states side effects, exceptional skipped reasons, return funnel fields, and the alternative for preview-only behavior. An agent has enough context to decide whether to call it and what to expect.

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 description coverage is 100%, so the parameter semantics baseline is 3. The schema already documents the lane enum (key) and specifies that playId comes from gtm_list_plays. The description adds general run semantics but no additional per-parameter detail beyond what the schema 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 is precise: 'run ONE lane immediately' through the production tick code, with explicit force semantics. It clearly names the resource (a lane) and the action (run), and distinguishes itself from gtm_preview_source, so an agent can identify this tool without ambiguity.

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 gives an explicit call condition: 'Only call it when the human asked for a run in this conversation.' It also names the alternative for non-spending lookups: 'to look without spending, use gtm_preview_source.' This is clear when/when-not guidance with no inference required.

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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