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fabdendev

dagster-mcp

by fabdendev

Get Run Stats

get_run_stats

Retrieve per-step execution stats for a Dagster run—timing, materialized assets, and expectation results—to spot slow steps and verify pipeline outcomes.

Instructions

Get per-step execution statistics for a run: timing, materializations, and expectations.

Returns runId, status, and a stepStats array where each entry has: stepKey, status, startTime, endTime, materializations (with labels), and expectationResults (with success flag and labels).

When to use: to find slow steps (compare startTime/endTime), check which steps materialized assets, or verify expectation results. For failed runs, prefer get_run_failure_summary which includes step stats alongside error details and suggestions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
envNo
run_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.8.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does add helpful behavioral context by detailing the returned stepStats structure and practical use cases, and the 'Get' verb implies a read operation. However, it does not explicitly state read-only status, side-effect profile, permission requirements, or rate limits, leaving gaps in operational 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 front-loaded with the core purpose, followed by a structured return summary and a clear usage paragraph. It is well organized, though the return-field detail is somewhat redundant given the presence of an output schema, and could be tighter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter read tool with an output schema and no annotations, the description covers purpose, usage, return shape, and sibling routing well. However, it leaves the optional env parameter completely unexplained and provides no explicit safety or permission context, so important gaps remain for correct invocation.

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

Parameters1/5

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

Schema description coverage is 0% for both input parameters, and the description does not explain the required run_id or the optional env parameter. It mentions a 'run' conceptually but adds no format, default, or selection semantics beyond what the bare schema already encodes, so the parameter gap is entirely unaddressed.

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 specific verb and resource ('Get per-step execution statistics for a run') and enumerates the data returned (timing, materializations, expectations). It also explicitly distinguishes itself from a sibling, get_run_failure_summary, making the scope clear without opening schemas.

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 gives explicit when-to-use guidance: find slow steps, check which steps materialized assets, or verify expectation results. It also names the preferred alternative for failed runs and the condition that selects it, leaving nothing to inference.

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