Skip to main content
Glama
fabdendev

dagster-mcp

by fabdendev

Get Tick History

get_tick_history

Retrieve recent tick history for a Dagster schedule or sensor to detect silent failures, including skipped, failed, or empty-run ticks.

Instructions

Get recent tick history for a schedule or sensor — essential for detecting silent failures.

  • instigator_name: exact name of the schedule or sensor (from list_schedules/list_sensors)

  • instigator_type: 'SCHEDULE' or 'SENSOR'

  • limit: max ticks to return (default 20)

  • repository_name / location_name: optional, to disambiguate when the same name exists in several code locations (see list_schedules/list_sensors)

Returns per tick: tick_id, status (SUCCESS/FAILURE/SKIPPED), timestamp, error message (if failed), and run_ids (runs launched by this tick).

When to use: when a schedule or sensor is RUNNING but data is not being produced. Common patterns to look for:

  • All ticks SKIPPED: sensor condition not met, or misconfigured

  • Ticks with FAILURE status: the schedule/sensor code is erroring

  • Ticks with SUCCESS but empty run_ids: sensor evaluated but decided not to launch

  • Missing ticks: daemon may be unhealthy (check get_instance_status)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
envNo
limitNo
location_nameNo
instigator_nameYes
instigator_typeYes
repository_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.8.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does substantial work: it enumerates the returned fields (tick_id, status, timestamp, error, run_ids) and, more importantly, explains how to interpret each status/run_ids combination. It does not cover auth requirements, rate limits, or the read-only nature explicitly, so it falls just short of a 5.

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?

Front-loaded with the core purpose, then organized into a parameter block and a diagnostic block. Every bullet on interpretation carries signal, though the description runs somewhat long relative to a six-parameter read tool.

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?

An output schema exists, so return-value structure need not be spelled out, yet the description still adds interpretive meaning on top of it. Nearly complete for correct invocation, with the undocumented 'env' parameter the only notable omission.

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?

Schema description coverage is 0%, so the description must compensate, and it documents five of six parameters including the effective enum for instigator_type ('SCHEDULE' or 'SENSOR') and the disambiguation role of repository_name/location_name. The 'env' parameter is never mentioned, leaving a real gap.

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?

States a specific verb and resource ('Get recent tick history for a schedule or sensor') and immediately frames the diagnostic goal ('detecting silent failures'). This cleanly separates it from get_runs/get_run_logs, which operate on runs rather than ticks.

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?

Explicit 'When to use: when a schedule or sensor is RUNNING but data is not being produced' plus four named diagnostic patterns that map observed results to causes. It also routes to a sibling alternative ('Missing ticks: daemon may be unhealthy — check get_instance_status') and names list_schedules/list_sensors as the source of valid names.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.