Files
mcp-server/spark_executor/tools/status.py
T
ClaudeandClaude Fable 5 6cf68439a2 feat: add external job tools + improve LLM-facing tool descriptions
Add 3 new MCP tools for inspecting YARN applications NOT submitted
through this service: get_external_job_logs, get_external_job_status,
get_external_job_result. Each takes application_id + connection_name
and queries YARN directly, bypassing the local JobStore.

- spark_executor/tools/external_jobs.py: 3 tool functions
- spark_executor/tools/requests.py: 3 new Pydantic body models
  (ExternalJobLogsRequest, ExternalJobStatusRequest,
  ExternalJobResultRequest)
- spark_executor/server.py: 3 new POST routes with explicit operation_id
- tests/unit/test_external_jobs.py: 7 unit tests
- tests/integration/test_mcp_routes.py: assert 20 tool routes
- README.md: list the 3 new tools

To make the LLM pick the right tool and not guess at field values,
also:

- Add Pydantic field descriptions for 22 fields across 8 request models
  (SaveConnectionRequest, UpdatePendingJobRequest, GetJobLogsRequest,
  JobIdRequest, PendingIdRequest, ConnectionNameRequest, plus the new
  ExternalJob*Request models).
- Update 12 route descriptions with cross-references, prerequisite
  context, and 400 behavior notes.
- Refactor _unknown_job_error: an input that looks like a YARN
  application_id (starts with 'application_') now returns HTTP 400
  (ValueError) with a hint message naming the right external tool;
  other not-found cases still return 404 (KeyError). This catches the
  common LLM mistake of passing application_id to the internal
  get_job_* / kill_job tools.
- 4 new unit tests for the 400 behavior.

Tests: 356 passed (up from 242).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-08 20:03:40 +08:00

37 lines
1.3 KiB
Python

# coding=utf-8
"""
@Time :2026/6/24
@Author :tao.chen
"""
from common.logging import logger
from spark_executor.core.yarn_client import YarnClientConfig
from spark_executor.core.job_store import JobStore
from spark_executor.core.yarn_client import get_application_status
from spark_executor.models import JobStatus
from spark_executor.tools.connections import store as conn_store
from spark_executor.tools.logs import _unknown_job_error
store = JobStore()
def get_job_status(job_id: str) -> JobStatus:
"""Query YARN for a job's current status.
`job_id` accepts either the local job_id (returned by
confirm_submit_job) or the YARN application_id.
"""
logger.debug(f"get_job_status enter job_id={job_id}")
job = store.get_either(job_id)
if job is None:
raise _unknown_job_error(
job_id,
external_tool_hint="get_external_job_status(application_id, connection_name)",
)
conn = conn_store.get(job.connection)
if conn is None:
raise KeyError(f"Connection not found: {job.connection}")
config = YarnClientConfig.from_connection(conn)
state, raw = get_application_status(job.application_id, config)
logger.info(f"get_job_status ok job_id={job.job_id} application_id={job.application_id} state={state}")
return JobStatus(application_id=job.application_id, state=state, raw=raw)