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Run schedule now

run_schedule_now

Fire a schedule ONCE, immediately, WITHOUT publishing — so you can test a draft before it goes live (parallels preview_dynamic_endpoint). Runs the version's workflow (omit version for the latest draft) and returns a reference to the workflow run it triggered; resolve its outcome with get_workflow_run. Unlike an automatic fire, this one is captured in FULL — output and traces intact — so it is also how you reproduce something a due firing only sampled. This executes the workflow (may have side effects).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugNothe schedule slug (provide this or schedule_id)
versionNospecific version to fire; omit (0) for the latest version (the draft)
schedule_idNothe schedule id (provide this or slug)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
run_idYes
statusNo
messageNo
versionYes
dispatched_atYes
scheduled_forYes

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

The description transparently notes that the tool executes the workflow and may have side effects, which is important behavioral context. It also clarifies that it does not publish and captures full output and traces. While it does not mention auth requirements or rate limits, the provided annotations (openWorldHint, destructiveHint) are not contradicted and the side-effect warning adds meaningful transparency.

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 moderately concise and each sentence contributes useful information: purpose, execution behavior, full-capture distinction, and side-effect warning. The use of emphasis and the slight repetition around 'draft' and 'automatic fire' adds a little verbosity but does not obscure meaning.

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?

Given the tool's moderate complexity and the existence of an output schema, the description sufficiently covers the main behavior, return reference, and side effects. It does not cover every edge case (e.g., both slug and schedule_id provided, or schedule not found), but it is complete enough for typical usage and aligns with the stated annotations.

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

Parameters4/5

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

The schema already covers the three parameters with 100% description coverage, so the baseline is 3. The description adds useful semantic nuance for the version parameter by explaining that omitting it targets the latest draft, which is valuable beyond the schema text. It does not deeply clarify slug vs. schedule_id selection, but the schema already handles that.

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 fires a schedule once, immediately, and without publishing, for testing drafts before they go live. It also explains that it runs the version's workflow and returns a reference to the triggered run, making the purpose specific and distinct from publishing or automatic firing.

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 explicit guidance on when to use the tool: to test a draft before going live and to reproduce a run with full output and traces, unlike an automatic fire which only samples. It also directs the user to get_workflow_run for resolving the outcome, offering clear usage context.

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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TDQS

A4/5.0
Disambiguation5/5

Every resource family follows the same verb+noun pattern and each tool name uniquely identifies a resource-action pair (create_app vs create_app_version vs update_app vs publish_app). Closest overlaps like analyze_resource vs get_resource_graph and patch_datafile vs update_datafile are explicitly differentiated by their descriptions, so misselection risk is low despite the scale.

Naming Consistency5/5

Names are almost uniformly verb_noun snake_case with a consistent lifecycle vocabulary: create/get/update/delete/list/publish/unpublish/version. Minor outliers like whoami and run_schedule_now are idiomatic and do not break the predictability of the set.

Tool Count1/5

At 93 tools this far exceeds the calibration's 50+ extreme-mismatch case. The count is inflated by repeating create/get/update/delete/version/publish/unpublish across ten resource families; even though each family is systematic, the combined surface is very hard for an agent to navigate and keep in context.

Completeness4/5

Core CRUD/publish/version lifecycles are present for apps, workflows, endpoints, schedules, schemas, datafiles, and api templates, and dependency analysis is well covered. However, secret creation/updating, asset upload, custom-domain deletion, and version-range enumeration for several resource types are absent or left to the external dashboard, so agents hit a few manual dead ends.

Resources