feat(result): get_job_result tool for terminal view of Spark job

`get_job_status` returns YARN state + the raw response blob, so the
terminal fields (finalStatus, diagnostics, trackingUrl, startedTime,
finishedTime) are buried inside `raw` and not surfaced in a structured
form. Add a new tool that parses them.

`get_job_result(job_id)` reuses `yarn_client.get_application_status` and
extracts:
  - finalStatus  (SUCCEEDED / FAILED / KILLED / UNDEFINED)
  - diagnostics  (YARN final message)
  - tracking_url (Spark Web UI)
  - started_time / finished_time (epoch ms)

All five fields are optional: running jobs have no `finishedTime`, and
older YARN versions (CDH 5 / H2) may omit some fields. Missing fields
stay None — never raise.

Coexistence with `get_job_status` is intentional: the latter is for
polling the running YARN state, the former is the terminal view.

Tests: 4 cases — happy path, running job (no finishedTime), bare-minimum
raw (all optionals None), unknown job_id raises KeyError. uv run pytest
-> 171 passed.

spark_executor/models.py

@@ -24,6 +24,16 @@ class JobStatus(BaseModel):
     raw: str = Field(default="")
 
 
+class JobResult(BaseModel):
+    application_id: str
+    state: str
+    final_status: str | None = None
+    diagnostics: str | None = None
+    tracking_url: str | None = None
+    started_time: int | None = None
+    finished_time: int | None = None
+
+
 class SubmitResult(BaseModel):
     job_id: str
     application_id: str

spark_executor/server.py

@@ -37,6 +37,8 @@ from spark_executor.tools.submit import (
     prepare_submit_job,
 )
 
+from spark_executor.tools.result import get_job_result
+
 app = FastAPI(title="Spark Executor MCP", version="0.0.1", description="Spark Executor MCP Server")
 
 
@@ -149,6 +151,20 @@ def _get_job_status(req: JobIdRequest):
     return get_job_status(req.job_id)
 
 
+@app.post(
+    "/get_job_result",
+    summary="Query YARN for a job's terminal result view",
+    description=(
+        "Return a terminal-oriented view of a Spark job: final_status, "
+        "diagnostics, tracking_url, started_time, and finished_time. "
+        "This is distinct from get_job_status, which is for polling the "
+        "running YARN state and returns the raw YARN response."
+    ),
+)
+def _get_job_result(req: JobIdRequest):
+    return get_job_result(req.job_id)
+
+
 @app.post(
     "/get_job_logs",
     summary="Fetch aggregated container logs for a job",

spark_executor/tools/result.py

@@ -0,0 +1,36 @@
+# coding=utf-8
+"""
+@Time   :2026/6/26
+@Author :tao.chen
+"""
+import json
+
+from common.logging import logger
+from spark_executor.core.job_store import JobStore
+from spark_executor.core.yarn_client import get_application_status
+from spark_executor.models import JobResult
+
+store = JobStore()
+
+
+def get_job_result(job_id: str) -> JobResult:
+    logger.debug(f"get_job_result enter job_id={job_id}")
+    job = store.get(job_id)
+    if job is None:
+        raise KeyError(f"Unknown job_id: {job_id}")
+    state, raw = get_application_status(job.application_id, job.yarn_rm_url)
+    app = json.loads(raw).get("app", {})
+    result = JobResult(
+        application_id=job.application_id,
+        state=state,
+        final_status=app.get("finalStatus"),
+        diagnostics=app.get("diagnostics"),
+        tracking_url=app.get("trackingUrl"),
+        started_time=app.get("startedTime"),
+        finished_time=app.get("finishedTime"),
+    )
+    logger.info(
+        f"get_job_result ok job_id={job_id} application_id={job.application_id} "
+        f"state={state} final_status={result.final_status}"
+    )
+    return result