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fabdendev

dagster-mcp

by fabdendev

get_instance_status

Check Dagster instance health for monitoring. Identifies unhealthy daemons, queued run bottlenecks, and code location errors.

Instructions

Get a global health check of the Dagster instance. START HERE for any monitoring workflow.

Returns:

  • healthy: boolean — true only if all required daemons are healthy AND no code locations have errors

  • daemons: list of {type, healthy, last_heartbeat, required} for each daemon (scheduler, sensor, run coordinator, etc.)

  • queued_runs_count: number of runs waiting in queue (high count = bottleneck)

  • queued_runs_count_capped: true when the count is a floor rather than exact, which happens only on Dagster versions that do not report a total

  • code_location_errors: list of {name, error} for locations that failed to load

When to use: as the FIRST call in any diagnostic or monitoring flow. If healthy=false, check daemons for unhealthy entries and code_location_errors for loading failures. Follow up with list_code_locations or get_runs as needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
envNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observedv0.8.0

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses the return structure in detail (healthy, daemons, queued_runs_count, queued_runs_count_capped, code_location_errors) and explains the meaning of 'healthy' and the cap condition. It implies a read-only operation but does not explicitly state side effects, permissions, or error cases beyond code location errors. This is strong but not exhaustive, hence a 4.

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 well structured: a one-sentence purpose with emphasis ('START HERE'), a clear bulleted list of return fields with explanations, and a dedicated 'When to use' paragraph. It front-loads the key message and wastes no words. Every sentence adds value, and the length is justified by the detailed return spec.

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?

The return values are fully explained (healthy logic, daemon list shape, queued runs count and cap, code location errors), and follow-up tools are suggested. There is an output schema present, which likely covers the return types, so the description doesn't need to repeat that. However, the env parameter is unexplained, and there is no mention of how to handle unreachable instances or authentication. These gaps reduce completeness slightly.

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

Parameters2/5

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

The tool has one parameter (env) with zero schema description coverage (0%). The description never mentions env, its purpose, or expected values. This is a gap: an agent cannot correctly set this parameter based on the description alone. While the parameter is optional, the lack of any guidance is a notable omission.

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 states a clear, specific purpose: 'Get a global health check of the Dagster instance.' It defines the resource (instance) and the action (get health check), and explicitly positions itself as the first step in monitoring workflows. It distinguishes itself from siblings by framing its global scope and hinting at follow-ups with list_code_locations or get_runs.

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?

It explicitly states when to use: 'as the FIRST call in any diagnostic or monitoring flow.' It also gives conditional logic: if healthy=false, check daemons and code_location_errors, and suggests follow-up tools. This is clear, actionable guidance that tells the agent exactly when to invoke it and what to do next.

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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