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dagster-mono-mcp

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dagster-mono-mcp

Dagster를 위한 단일 도구 MCP 서버입니다. 하나의 도구, 다섯 가지 작업, 최소한의 토큰 오버헤드를 제공합니다.

Dagster에는 공식 MCP 서버가 있지만, 쉽게 작동하게 만들기가 어려웠습니다. 이 서버는 LLM으로 파이프라인을 디버깅할 때 실제로 필요한 작업인 실행 목록 확인, 실행 세부 정보 검사, 로그 읽기, 그리고 기타 모든 작업을 위한 원시 GraphQL 쿼리를 수행할 수 있는 최소한의 대안입니다.

Zero Trust 뒤에 있는 Dagster 인스턴스를 위해 Cloudflare Access를 지원하며, 이는 Dagster 인스턴스를 보호하는 쉽고 무료인 방법입니다.

설치

Claude Code

프로젝트 범위 지정 (.mcp.json을 프로젝트 루트에 생성):

{
  "mcpServers": {
    "dagster": {
      "command": "npx",
      "args": ["-y", "github:pjatx/dagster-mono-mcp"],
      "env": {
        "DAGSTER_GRAPHQL_URL": "https://dagster.example.com/graphql",
        "CF_ACCESS_CLIENT_ID": "your-client-id",
        "CF_ACCESS_CLIENT_SECRET": "your-client-secret"
      }
    }
  }
}

전역 설정 (~/.claude.json — 모든 프로젝트에서 사용 가능):

{
  "mcpServers": {
    "dagster": {
      "command": "npx",
      "args": ["-y", "github:pjatx/dagster-mono-mcp"],
      "env": {
        "DAGSTER_GRAPHQL_URL": "https://dagster.example.com/graphql"
      }
    }
  }
}

Cursor

프로젝트 루트의 .cursor/mcp.json에 추가:

{
  "mcpServers": {
    "dagster": {
      "command": "npx",
      "args": ["-y", "github:pjatx/dagster-mono-mcp"],
      "env": {
        "DAGSTER_GRAPHQL_URL": "https://dagster.example.com/graphql",
        "CF_ACCESS_CLIENT_ID": "your-client-id",
        "CF_ACCESS_CLIENT_SECRET": "your-client-secret"
      }
    }
  }
}

Windsurf

~/.codeium/windsurf/mcp_config.json에 추가:

{
  "mcpServers": {
    "dagster": {
      "command": "npx",
      "args": ["-y", "github:pjatx/dagster-mono-mcp"],
      "env": {
        "DAGSTER_GRAPHQL_URL": "https://dagster.example.com/graphql",
        "CF_ACCESS_CLIENT_ID": "your-client-id",
        "CF_ACCESS_CLIENT_SECRET": "your-client-secret"
      }
    }
  }
}

Related MCP server: MCP Access OAuth Server

환경 변수

변수

기본값

필수 여부

DAGSTER_GRAPHQL_URL

http://localhost:3000/graphql

아니요

CF_ACCESS_CLIENT_ID

—

아니요 (CF Access 뒤에 있는 경우 필수)

CF_ACCESS_CLIENT_SECRET

—

아니요 (CF Access 뒤에 있는 경우 필수)

사용법

작업 디스패치를 포함하는 단일 도구 dagster:

실행 목록 확인

{"action": "runs"}
{"action": "runs", "status": "FAILURE"}
{"action": "runs", "job": "my_job", "limit": 5}

실행 세부 정보

{"action": "run", "id": "<run-id>"}

이벤트 로그

{"action": "logs", "id": "<run-id>"}
{"action": "logs", "id": "<run-id>", "limit": 100}

원시 GraphQL

{"action": "graphql", "query": "{ version }"}
{"action": "graphql", "query": "query($id: ID!) { runOrError(runId: $id) { __typename } }", "variables": {"id": "abc123"}}

도움말

{"action": "help"}

사용 가능한 상태, 모든 매개변수, 예제 쿼리를 포함한 전체 문서를 반환합니다.

설계

이 프로젝트는 모노 도구(mono-tool) 패턴을 따릅니다. 여러 개의 작은 도구 대신 작업 디스패치를 사용하는 하나의 MCP 도구를 제공합니다. 도구 수가 적으면 LLM의 토큰 오버헤드가 줄어들고 도구 선택이 더 간단해집니다. 모델이 비슷하게 들리는 수십 개의 도구 중에서 고민할 필요가 없습니다. LLM이 MCP 서버와 상호작용하는 방식을 재고한 Cloudflare의 Code Mode 게시물에서 영감을 받았습니다.

개발

npm install
npm run build    # esbuild bundle -> dist/index.js
npm start        # run the MCP server (stdio transport)

라이선스

MIT

Available Tools

1 tool
dagsterC

Dagster runs & debugging. Actions: runs, run, logs, graphql, help

{"action": "runs"} -> recent runs {"action": "runs", "status": "FAILURE"} -> failed runs {"action": "run", "id": "abc123"} -> run details {"action": "logs", "id": "abc123"} -> run logs {"action": "graphql", "query": "{ version }"} -> raw GraphQL {"action": "help"} -> full documentation

ParametersJSON Schema
NameRequiredDescriptionDefault
actionYes
idNo
jobNo
statusNo
limitNo
queryNo
variablesNo

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what each action does (e.g., 'recent runs', 'run details'), but lacks critical behavioral traits such as authentication requirements, rate limits, error handling, or whether actions are read-only or destructive. The examples imply read operations but don't explicitly state safety.

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 appropriately sized and front-loaded with a brief purpose statement followed by action examples. Each sentence earns its place by illustrating usage, though it could be more structured (e.g., bullet points) and the initial line is somewhat vague.

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

Completeness2/5

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

Given the complexity (7 parameters, no output schema, no annotations), the description is incomplete. It covers actions and some parameters but misses details on return values, error cases, and full parameter semantics. Without annotations or output schema, more behavioral and contextual information is needed for effective use.

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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains what each action does and provides example parameter combinations (e.g., 'runs' with 'status', 'run' with 'id'), clarifying how parameters interact. However, it doesn't cover all 7 parameters (e.g., 'job', 'limit', 'variables' are unexplained), so it doesn't fully compensate for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool is for 'Dagster runs & debugging' and lists five actions, which gives a general purpose but lacks specificity about what Dagster is or what resources it operates on. It distinguishes between actions but doesn't clearly articulate the overall tool's function beyond listing sub-actions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, as there are no sibling tools mentioned. It lists actions with examples but doesn't explain prerequisites, contexts, or exclusions for usage.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observeddagster
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored across 1 tool

Disambiguation5/5

The single tool 'dagster' has clearly distinct actions (runs, run, logs, graphql, help) with no overlap in purpose. Each action targets a specific operation within the Dagster domain, making misselection impossible.

Naming Consistency5/5

The tool naming is perfectly consistent as there is only one tool, 'dagster', and its actions follow a clear, uniform pattern (e.g., 'runs', 'run', 'logs') without any mixing of conventions or styles.

Tool Count2/5

With only one tool, the server feels thin for a Dagster monitoring/debugging domain, as it bundles multiple distinct operations (e.g., querying runs, fetching logs, GraphQL) into a single tool. This may limit clarity and usability compared to a more granular tool set.

Completeness3/5

The tool covers core operations like retrieving runs, logs, and GraphQL queries, but lacks obvious lifecycle actions such as triggering new runs, pausing/resuming, or managing assets. This creates notable gaps for a full Dagster debugging workflow.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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