feat: add list_applications tool for YARN application enumeration

Add a new MCP tool that queries YARN's /ws/v1/cluster/apps endpoint
through a named Connection, returning a list of ApplicationSummary
records. Bypasses the local JobStore — useful for enumerating apps
that were not submitted through this service.

API:
  list_applications(
    connection_name: str,           # required, which YARN cluster
    state: str | None = None,       # YARN state filter: NEW/NEW_SAVING/
                                    # SUBMITTED/ACCEPTED/RUNNING/
                                    # FINISHED/FAILED/KILLED
    queue: str | None = None,       # YARN queue filter
    limit: int = 100,               # cap on returned apps (YARN has no
                                    # offset-based pagination; combine
                                    # state/queue filters for big clusters)
  ) -> list[ApplicationSummary]

Implementation:
  - yarn_client.list_applications(config, *, state, queue, limit) -> list[dict]
    Returns raw YARN app dicts; raises YarnError on 4xx/5xx; returns
    [] on 404 (no apps match). Uses the existing _request helper,
    which now accepts a "params" kwarg for query strings (one-line
    additive change).
  - external_jobs.list_applications(connection_name, state, queue, limit)
    -> list[ApplicationSummary]. Looks up the Connection, builds the
    YarnClientConfig, calls the yarn_client function, maps each raw
    YARN dict to ApplicationSummary (mirroring the manual field-mapping
    style of get_job_result). The yarn_client function is imported
    as "list_applications_yarn" to avoid name collision.
  - ApplicationSummary: 12-field Pydantic model with snake_case names
    (application_id, name, user, queue, state, final_status,
    application_type, application_tags, started_time, finished_time,
    tracking_url, progress). Unused YARN fields (memorySeconds,
    vcoreSeconds, preemptedResource*, etc.) are not exposed.
  - ListApplicationsRequest: Pydantic body model with Field(description=)
    for LLM-facing schema.
  - /list_applications route registered with operation_id=
    "list_applications", placed next to the other external YARN tools.

Tests:
  - 8 new unit tests in test_external_jobs.py (happy path, state/queue/
    limit pass-through, default limit, empty list, missing connection,
    full field mapping).
  - test_mcp_routes.py: assert 23 tool routes.
  - README: list_applications row added to the Spark Executor table.

Tests: 390 passed (was 382, +8 net).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

spark_executor/models.py

@@ -48,6 +48,28 @@ class FetchUrlResult(BaseModel):
     truncated: bool = False
 
 
+class ApplicationSummary(BaseModel):
+    """A YARN application summary from /ws/v1/cluster/apps.
+
+    Field names are mapped from the YARN JSON keys to clearer
+    snake_case names by the tool function. Unused YARN fields
+    (memorySeconds, vcoreSeconds, preemptedResource*, etc.) are
+    not exposed — the LLM doesn't need them.
+    """
+    application_id: str
+    name: str
+    user: str
+    queue: str
+    state: str
+    final_status: str | None = None
+    application_type: str | None = None
+    application_tags: str = ""
+    started_time: int = 0
+    finished_time: int = 0
+    tracking_url: str | None = None
+    progress: float | None = None
+
+
 class Connection(BaseModel):
     name: str
     # Defaults to "yarn" because that's the literal string spark-submit wants