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Dagster MCP

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作者:fabdendev

MCP server that wraps the Dagster GraphQL API — monitor and operate runs, assets, schedules, sensors, and backfills from any MCP client

ai-agentdagsterdata-pipelinesdataopsgraphqlllmmcpmodel-context-protocol

01 / 选择客户端

02 / 连接前的准备

认证方式尚未指定。连接前请查看项目说明。

安装项目所需的运行环境,并确保客户端能够使用它。

项目说明

输入项目说明中的变量名,用逗号分隔。只输入名称,不要输入密钥值。

03 / 添加配置

来源:根据公开安装说明生成

claude mcp add --transport stdio 'dagster-mcp' -- 'uvx' 'dagster-mcp'

替换占位符后在终端中运行。

04 / 在客户端中确认

打开客户端的 MCP 设置,确认服务器已连接并列出工具。复制配置不代表连接成功。

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安全概况

已认领和已认证的服务器每周接受一次静态扫描,显示代码能触及的范围(外部服务、环境变量、shell 命令、代理配置目录)以及带有已知公告的依赖。认领此列表即可获得。 安全概况的工作原理

27 个工具中显示 27 个

文档中列出的工具 (27)

内容来自项目文档。服务器握手不会验证每个工具的说明或行为。

backfill_assets

Launch a partition backfill by asset selection with optional run config; respects each asset's BackfillPolicy server-side

get_asset_details

Get description, upstream/downstream dependencies, partitions, latest materialization

get_asset_health

Consolidated health view — staleness, freshness policy, last run status (works with single asset or entire group)

get_instance_status

Start here — global health: daemon status, queued run count, code location errors

get_recent_materializations

Get materialization history with metadata for an asset

get_run_failure_summary

Consolidated failure diagnosis — failed steps, root cause error, step durations, and suggestions in one call

get_run_logs

Get structured log events with pagination and optional level filtering (ERROR, WARNING, INFO); EngineEvent events include metadataEntries

get_run_stats

Get per-step execution stats: timing, materializations, expectation results

get_run_status

Get status, config, tags, and run lineage (re-execution chain via rootRunId/parentRunId)

get_runs

List recent runs, filter by job name and/or status

get_tick_history

Tick-by-tick history for a schedule or sensor — essential for detecting silent failures. Accepts optional repositoryname / locationname to disambiguate a name shared by several code locations

launch_job

Launch a named job; assetkeys remains supported for compatibility and is sent through GraphQL assetSelection, but the two-step asset workflow is preferred

launch_job_with_partitions

Launch a partitioned job for one or more partition keys; creates a backfill (supports fromfailure to retry only failed steps)

list_backfills

List recent backfills with status and partition progress

list_code_locations

List all code locations and their load status

list_jobs

List jobs across code locations, optionally filtered by exact repositoryname and/or locationname (use to find names for launchjob)

list_schedules

List schedules with status (RUNNING/STOPPED), cron, target job, next tick

list_sensors

List sensors with status and target jobs

materialize_assets

Launch concrete, unpartitioned asset keys with run config and tags; infers one compatible repository/job, includes compatible checks, and expands required non-subsettable multi-asset neighbors

reload_code_location

Reload a code location after deploy

resolve_asset_selection

Resolve key/group/tag/kind/owner predicates, wildcards, boolean logic, roots/sinks, and lineage traversal into concrete asset keys without launching anything

search_assets

Discover assets by key prefix or group name

start_schedule

Start (enable) a schedule so it launches runs on its cron

start_sensor

Start (enable) a sensor so it resumes evaluating

stop_schedule

Stop (disable) a schedule — persists across restarts; does not terminate in-flight runs

stop_sensor

Stop (disable) a sensor — the fix for a runaway or erroring sensor found via gettickhistory

terminate_run

Stop a stuck or runaway run

工具变更历史

比较相同配置的完整检查。只列出工具,未调用工具。此处不测量输入结构的变化。

尚无完整工具检查。