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refactor(mcp): rename generate_job_file to write_job_file to match what it does

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The MCP tool was named `generate_job_file` from Stage 2 but it does
NOT generate PySpark code — the calling LLM writes the code in its own
context, and this tool only persists it to a file under
SPARK_EXECUTOR_JOBS_DIR so `spark-submit` can see it. The misleading
`generate_` prefix sent agents (and humans) looking for a code
generator that doesn't exist.

This commit folds three related polish changes into one (split later
with rebase -i if you want them as separate history):

  1. The rename itself:
     - `tools/generate.py`  →  `tools/write_job.py`
     - `generate_job_file`  →  `write_job_file`
     - `GenerateJobFileRequest`  →  `WriteJobFileRequest`
     - `/generate_job_file` route  →  `/write_job_file`
     - `operation_id="generate_job_file"`  →  `operation_id="write_job_file"`
     The internal helper `core.job_writer.write_job_file` (which just
     writes bytes to disk with no SQL guard) is imported with an
     `_write_to_disk` alias to avoid the name collision with the
     MCP-exposed function in the same module.
     The description for the tool now explicitly states 'this tool
     does NOT generate PySpark code. The calling LLM is expected to
     have already written the code; this tool only persists it.'

  2. Skill for LLM agents operating the service
     (`docs/superpowers/skills/spark-executor-mcp-operate/SKILL.md`,
     449 lines). Covers the 16 tools, the two-step prepare/confirm
     flow, the dual-ID contract (job_id vs application_id), the
     PendingSubmission state machine, the Connection profile, the
     job-file workflow, the error reference, common pitfalls, and a
     full end-to-end word-count example.

  3. Default `executor_memory` lowered 4G → 2G
     (`_DEFAULTS_TO_CONFIRM` in `server.py`). Mirrors the matching
     change in `test_mcp_routes.py` and the 5 unit tests that
     reference the default. Aligns with the lighter workloads the
     service is sized for in its current container profile.

Also tracked in git for the first time:
  - `docs/superpowers/plans/2026-06-24-spark-executor-mcp.md`
    (the original Stage 1/2/3 design plan, updated to use the new
    tool name throughout).

Test rename:
  - `tests/unit/test_generate_tool.py`  →  `test_write_job_tool.py`
  - the new test file picks up an extra assertion that the SQL guard
    rejects a `DROP TABLE` statement at write time.

243 tests pass (was 242; +1 new SQL-guard assertion). Zero regressions.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
Claude
2026-06-29 19:13:29 +08:00
co-authored by Claude Fable 5
parent 0fef77c0b8
commit 523e6a9c76
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---
name: spark-executor-mcp-operate
description: Operation guide for the spark-executor-mcp service — 16 MCP tools that submit, monitor, fetch logs from, and kill PySpark jobs on YARN, plus connection-profile and script-file management. USE THIS SKILL whenever the user wants to do anything with the spark-executor-mcp MCP service: submit a Spark/PySpark job to YARN, check job status or result, fetch container logs, kill a running job, save/list/get/delete a YARN connection, or write/read/update a PySpark script file. Also use it when the user asks generally about Spark, YARN, pyspark, cluster jobs, or distributed compute via this service. Do NOT guess tool names or invent a submit flow — read this skill first to learn the two-step prepare/confirm flow, the dual-ID contract (job_id vs application_id), and the SQL guard / path restrictions. The MCP service does NOT generate PySpark code — the calling LLM writes the code, then uses `write_job_file` to persist it on disk before submitting.
---
# spark-executor-mcp 操作指南
Spark-executor-mcp is an MCP (Model Context Protocol) service that exposes
**16 tools** for managing PySpark jobs on YARN. It runs as a FastAPI app
mounted at `/spark-executor-mcp`; clients (typically LLM agents) call the
tools via the standard MCP `tools/call` flow.
This skill is the canonical reference for an LLM agent using the service.
It does **not** describe implementation details — for that, see the source
in `spark_executor/`.
---
## 0. Mindset before you start
Three things you MUST internalize before calling any tool:
1. **The submit flow is two-step: `prepare_submit_job` then `confirm_submit_job`.**
Prepare is cheap and reversible (just snapshots params + writes JSON to
disk). Confirm is irreversible — it actually invokes `spark-submit` and
a YARN application starts running. Never skip the prepare step, even
when the user is in a hurry.
2. **Every Job has two IDs that are NOT interchangeable everywhere.**
- `job_id` — local, 12-char hex (`a1b2c3d4e5f6`), generated by the
service. Used for: looking up the Job record, all MCP tool calls.
- `application_id` — YARN's own ID (`application_17400000001_0001`).
Used for: YARN UI, `tracking_url`, the YARN REST API.
The service's 4 job-lifecycle tools accept **either** ID (after the
2026-06-29 fix), but other systems (YARN UI, kubectl-style scripts)
only know `application_id`. **Always store both** from every
`confirm_submit_job` response.
3. **The LLM writes the PySpark code. The MCP service writes the file.**
`write_job_file` is a file writer with SQL-safety checks, not a
code generator. You compose the code in your context, then call
`write_job_file(code=...)` to drop it on the container's
filesystem where `spark-submit` can see it.
---
## 1. Tool index
| Operation ID | HTTP | Group | Purpose |
|---|---|---|---|
| `save_connection` | POST | Connection | Upsert a named YARN connection (master, deploy_mode, spark_conf, …) |
| `list_connections` | POST | Connection | List all saved connections |
| `get_connection` | POST | Connection | Fetch one connection by name |
| `delete_connection` | POST | Connection | Delete a connection by name |
| `write_job_file` | POST | Job file | Write LLM-written PySpark code to disk |
| `read_job_file` | POST | Job file | Read a script file back (capped at 1 MB) |
| `update_job_file` | POST | Job file | Overwrite a script file (capped at 1 MB) |
| `prepare_submit_job` | POST | Submit | Snapshot a job to `PendingSubmission`; **does NOT submit** |
| `confirm_submit_job` | POST | Submit | Actually invoke `spark-submit` for a `pending_id` |
| `update_pending_job` | POST | Submit | Edit a `PENDING` submission (script_path, queue, …) |
| `cancel_pending_job` | POST | Submit | Cancel a `PENDING` submission |
| `list_pending_jobs` | POST | Submit | List every PendingSubmission regardless of status |
| `get_pending_job` | POST | Submit | Fetch one PendingSubmission by id |
| `get_job_status` | POST | Job lifecycle | Query YARN for a job's live state (RUNNING / SUCCEEDED / …) |
| `get_job_result` | POST | Job lifecycle | Terminal-oriented view: final_status, diagnostics, timing |
| `get_job_logs` | POST | Job lifecycle | Aggregated container logs (default last 5000 chars) |
| `kill_job` | POST | Job lifecycle | PUT state=KILLED to YARN REST for the app |
(Total: 16 MCP tools + `/health` GET, which is not exposed as a tool.)
---
## 2. The canonical happy path: submit a PySpark job
```
1. save_connection # one-time, per cluster
2. write_job_file # write the LLM's PySpark to disk
3. read_job_file # (optional) sanity-check what was written
4. prepare_submit_job # snapshot + persist; no YARN call yet
5. confirm_submit_job # NOW spark-submit runs
6. get_job_status / logs # poll while running
7. get_job_result # terminal view when done
```
### 2.1 Save a connection (one-time per cluster)
```python
save_connection(
name="prod",
master="yarn",
deploy_mode="cluster",
yarn_rm_url="http://rm.example.com:8088",
spark_conf={"spark.sql.shuffle.partitions": "200"},
# optional: auth_type="kerberos", ssl_verify=False, ...
)
```
**`name`** is the lookup key — keep it short and stable. Re-calling
`save_connection` with the same `name` overwrites; the original `yarn_rm_url`
and `spark_conf` are **snapshot into the PendingSubmission at prepare time**
and survive later edits to the connection (this is intentional — the user
can re-point the connection at a different cluster without retargeting
in-flight submissions).
### 2.2 Write the script to disk
The LLM writes the PySpark code. Then:
```python
script_path = write_job_file(code=PYSPARK_SOURCE)["script_path"]
# Returns {"script_path": "/absolute/path/in/container.py", "bytes": N}
```
The path lives inside the container's `SPARK_EXECUTOR_JOBS_DIR` (default
`./data/jobs/`). For pre-existing scripts on the host, mount the host
directory into the container and skip this step — pass the in-container
path directly to `prepare_submit_job`.
### 2.3 Prepare (snapshot, no YARN call)
```python
prepare_submit_job(
connection="prod",
script_path=script_path,
app_name="daily-aggregation", # REQUIRED, no default
queue="default", # these 4 must be explicit
executor_memory="2G",
executor_cores=2,
num_executors=2,
# extra_args={"jars": "hdfs://...jar"}, # optional
)
# Returns {"pending_id": "p_a1b2c3", "status": "PENDING", "parameters": {...}}
```
**You MUST pass `queue`, `executor_memory`, `executor_cores`, `num_executors`
explicitly every time** — even though defaults exist server-side, the
service rejects implicit defaults with a 400 listing the assumed values.
This forces the agent (or the human reviewer) to see and approve what
will actually be submitted.
The response is the only chance to capture the `pending_id` you need for
`confirm_submit_job`. Store it.
### 2.4 (Optional) Edit before submit
If something needs to change between prepare and confirm:
```python
update_pending_job(
pending_id="p_a1b2c3",
queue="research",
# script_path can also be changed — but the new file MUST exist
# and pass the SQL guard
)
```
Refuses to edit anything that is no longer `PENDING`.
### 2.5 Confirm (the irreversible step)
```python
result = confirm_submit_job(pending_id="p_a1b2c3")
# Returns {"job_id": "a1b2c3d4e5f6",
# "application_id": "application_17400000001_0001",
# "tracking_url": "http://rm:8088/proxy/application_17400000001_0001/"}
```
**STORE BOTH IDS NOW.** Future calls to `get_job_status` / `get_job_logs`
/ `kill_job` accept either, but `application_id` is the one YARN itself
recognizes.
`confirm_submit_job` is **idempotent** on `SUBMITTED` (re-calling returns
the same record) and **retries** on transient `SparkSubmitError` (3
attempts with 5 s delay by default). It transitions to `FAILED` only
after all retries are exhausted.
### 2.6 Monitor / fetch / kill
```python
# While running
state = get_job_status(job_id="a1b2c3d4e5f6").state
logs = get_job_logs(job_id="a1b2c3d4e5f6", tail_chars=10000)
# When done
result = get_job_result(job_id="a1b2c3d4e5f6")
# result.state -> "SUCCEEDED" / "FAILED" / "KILLED" / ...
# result.final_status -> "SUCCEEDED" / "FAILED" / "KILLED" / "UNDEFINED"
# result.diagnostics -> YARN's "why" string (often empty on success)
# result.tracking_url -> the same proxy URL confirm_submit_job returned
# To stop it
kill_job(job_id="a1b2c3d4e5f6")
```
All four accept **either** `job_id` or `application_id`. Pass whichever
you have on hand; the service tries `job_id` first, falls back to
`application_id`.
---
## 3. The PendingSubmission state machine
```
prepare_submit_job
|
v
+-------+ cancel_pending_job
|PENDING+----------> CANCELLED (terminal)
+---+---+
| confirm_submit_job
v
+----------+
| SUBMITTED| (terminal — kill via kill_job, not cancel)
+----------+
(or)
+--------+
| FAILED | <-- confirm_submit_job exhausted retries
+--------+
| confirm_submit_job (resets to PENDING, retries)
v
PENDING
```
- `cancel_pending_job` **refuses** to cancel `SUBMITTED` or `FAILED`
records — those are terminal; use `kill_job` instead.
- `update_pending_job` **refuses** to edit anything that is not
`PENDING`.
- `confirm_submit_job` on a `SUBMITTED` record is a no-op (returns the
same `job_id` / `application_id`). On a `FAILED` record, it resets
to `PENDING` and retries. On a `CANCELLED` record, it raises 400.
---
## 4. The dual-ID contract
| ID | Where it comes from | What it's good for |
|---|---|---|
| `job_id` | service-generated, 12-char hex (`a1b2c3d4e5f6`) | Looking up the Job in this service's store; all 4 lifecycle tools |
| `application_id` | parsed from `spark-submit` stderr (`application_<ts>_<n>`) | YARN UI, YARN REST, `tracking_url`, talking to ops scripts |
**Both IDs are returned by `confirm_submit_job`.** Both are accepted by
`get_job_status` / `get_job_result` / `get_job_logs` / `kill_job`. **You
don't need to remember which to pass — but DO remember to store both
from the confirm response** because:
- If the Job store has been wiped (rare; only on data dir loss), only
`application_id` is still meaningful to YARN — you can't poll a YARN
app you can't identify.
- If you're going to paste a link to a teammate, `tracking_url` (derived
from `application_id`) is the human-friendly form.
If a lifecycle tool returns `KeyError("No Job found for id='X' …")`,
check that `X` came from a successful `confirm_submit_job` response.
The error message intentionally mentions both ID forms and shows an
example of each so you can self-diagnose.
---
## 5. Connection profiles
`Connection` is the cluster profile — it's the "where to submit" record.
Fields:
| Field | Required | Default | Notes |
|---|---|---|---|
| `name` | yes | — | The lookup key |
| `master` | yes | `"yarn"` | `"yarn"` / `"spark://…"` / `"k8s://…"` / `"local[N]"` |
| `deploy_mode` | yes | `"cluster"` | `"cluster"` or `"client"` |
| `yarn_rm_url` | no | env `YARN_RESOURCE_MANAGER_URL` | Required if you'll call `get_job_status` / `get_job_logs` / `kill_job` (the lifecycle tools need a YARN endpoint) |
| `spark_conf` | no | `{}` | Merged into `spark-submit --conf` at submit time |
| `ssl_verify` | no | env `SPARK_EXECUTOR_SSL_VERIFY_DEFAULT` | bool |
| `ssl_ca_bundle` | no | env `SPARK_EXECUTOR_SSL_CA_BUNDLE_DEFAULT` | Path to CA file |
| `auth_type` | no | `"none"` | `"none"` / `"simple"` / `"basic"` / `"kerberos"` |
| `auth_user` / `auth_password` | for `basic` | — | |
| `auth_principal` / `auth_keytab` | for `kerberos` (display only) | — | Real auth uses the system cache |
**Snapshot semantics:** when `prepare_submit_job` runs, it copies
`master` / `deploy_mode` / `spark_conf` / `yarn_rm_url` from the
**current** Connection into the `PendingSubmission`. Editing the
Connection later does **not** retarget an in-flight submission.
**Lifecycle tools use the Job's snapshotted `yarn_rm_url`** (set at
confirm time) — not the Connection's current value. If the RM moves
between confirm and a follow-up `get_job_logs`, you have a problem;
re-pointing the Connection does not help.
---
## 6. Job file workflow (`write_job_file` / `read_job_file` / `update_job_file`)
These are **filesystem I/O tools, not code generators**. The LLM in
context writes the PySpark code; these tools just persist it where
`spark-submit` can find it, with safety guards.
**Typical lifecycle:**
```
LLM composes code --> write_job_file(code=...) # write
LLM reviews code --> read_job_file(script_path=...) # verify
LLM revises code --> update_job_file(script_path=..., # overwrite
content=...)
LLM submits --> prepare_submit_job(script_path=...)
```
**Guards (these are why the tools exist instead of just `cat > file.sh`):**
- **SQL guard** — `write_job_file` and `update_job_file` both reject
code containing forbidden SQL (DROP, DELETE, UPDATE, TRUNCATE, ALTER,
GRANT, REVOKE, …). The check is conservative; if it false-positives,
rewrite to use only SELECT/INSERT or restructure.
- **Path restriction** — `update_job_file` only writes under
`SPARK_EXECUTOR_JOBS_DIR`. This blocks accidentally overwriting
host-mounted configs or the service's own files.
- **Size cap** — 1 MB on both `read_job_file` and `update_job_file`.
Use `read_job_file` for small scripts; for huge ones, mount the host
directory and skip these tools.
**For pre-existing scripts on the host:** mount the host dir into the
container (e.g. `-v /host/scripts:/app/scripts:ro` in `docker run`) and
pass the in-container path directly to `prepare_submit_job` — no need
to round-trip through the file tools.
---
## 7. Error reference
| Status | When | What to do |
|---|---|---|
| 400 | `prepare_submit_job` without explicit defaults; `cancel_pending_job` on terminal state; `update_pending_job` on non-PENDING; `prepare_submit_job` with missing/non-file `script_path`; SQL policy violation | Read the message — it almost always names the fix. Re-submit with corrected args. |
| 404 | `KeyError` from tool layer: unknown `connection`, `pending_id`, `job_id`, or `application_id` | The ID you sent doesn't match anything in the store. For lifecycle tools, the error message explicitly suggests both ID forms. |
| 422 | Pydantic validation: missing field, wrong type | Usually a typo or missing required arg. The response body lists every field error. |
| 500 | spark-submit subprocess failed and was not a `SparkSubmitError` (e.g. binary missing); YARN REST call failed | The tool logs the full traceback to `data/logs/error/...` — check there. |
The service translates exceptions at the FastAPI layer:
- `KeyError` → 404 (with the KeyError's string as `detail`)
- `ValueError` → 400
- Pydantic validation → 422
So when the server returns 404, the body `detail` field is the same
"Unknown job_id: …" / "Unknown connection: …" string the tool layer
raised.
---
## 8. Common pitfalls
| Pitfall | Why it bites | What to do |
|---|---|---|
| Skipping the prepare step | `confirm_submit_job` requires a `pending_id`; calling it without one returns 400 | Always prepare first |
| Passing implicit defaults | Server returns 400 listing what you didn't confirm | Pass `queue`, `executor_memory`, `executor_cores`, `num_executors` every time |
| `script_path` doesn't exist in the container | `prepare_submit_job` returns 400; the agent generated the path in its own context but the container's filesystem is different | Either call `write_job_file` first, or mount a host dir |
| SQL injection in the script | `write_job_file` / `update_job_file` / `prepare_submit_job` all 400 | Only use SELECT/INSERT; never DROP/DELETE/UPDATE |
| `cancel_pending_job` on a running YARN app | Returns 400 — submissions are terminal once submitted | Use `kill_job(job_id=...)` instead, which talks to YARN REST |
| Re-pointing a Connection and expecting in-flight jobs to follow | They don't — the Job snapshots `yarn_rm_url` at confirm time | Re-issue with a new `prepare_submit_job` if you actually need a different RM |
| Calling lifecycle tools with the wrong ID type | Used to fail; **fixed in the 2026-06-29 update** — both IDs are accepted | Just pass whichever you have; the service tries `job_id` first |
| "Session not found" at the MCP layer | Multi-worker gunicorn + in-process session state | Out of scope for this skill; the service warns on startup if `GUNICORN_WORKERS>1`; set `GUNICORN_WORKERS=1` or front with a sticky LB |
---
## 9. End-to-end example (LLM agent's perspective)
User: "Run a word count on `s3a://my-bucket/books/*.txt` and write the
result back to `s3a://my-bucket/wc/`."
```
# 1. Cluster profile (assumes this is a fresh setup)
save_connection(
name="prod",
master="yarn",
deploy_mode="cluster",
yarn_rm_url="http://rm.prod.example.com:8088",
spark_conf={
"spark.sql.shuffle.partitions": "200",
"spark.hadoop.fs.s3a.access.key": "...",
"spark.hadoop.fs.s3a.secret.key": "...",
},
)
# 2. Compose the PySpark code IN YOUR CONTEXT
pyspark_code = '''
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("wordcount").getOrCreate()
sc = spark.sparkContext
files = sc.wholeTextFiles("s3a://my-bucket/books/")
words = files.flatMap(lambda f: f[1].lower().split()) \\
.map(lambda w: (w, 1)) \\
.reduceByKey(lambda a, b: a + b)
df = words.toDF(["word", "count"])
df.write.mode("overwrite").parquet("s3a://my-bucket/wc/")
'''
# 3. Persist to disk
script_path = write_job_file(code=pyspark_code)["script_path"]
# 4. Sanity-check (optional but recommended)
contents = read_job_file(script_path=script_path)["content"]
# ... review in context ...
# 5. Prepare (snapshot — no YARN call yet)
pending = prepare_submit_job(
connection="prod",
script_path=script_path,
app_name="wordcount-books",
queue="default",
executor_memory="2G",
executor_cores=2,
num_executors=2,
)
pending_id = pending["pending_id"]
# 6. Confirm (NOW spark-submit runs)
result = confirm_submit_job(pending_id=pending_id)
job_id = result["job_id"] # STORE
application_id = result["application_id"] # STORE
tracking_url = result["tracking_url"] # STORE
# 7. Poll
import time
while True:
st = get_job_status(job_id=job_id).state
if st in ("SUCCEEDED", "FAILED", "KILLED", "FINISHED"):
break
time.sleep(30)
# 8. Final result
res = get_job_result(job_id=job_id)
print(res.final_status, res.diagnostics)
# If something went wrong and you want to see why:
# logs = get_job_logs(job_id=job_id, tail_chars=20000)
```
---
## 10. Reference paths (for the curious)
This skill is the **operation** guide. The architectural / implementation
details live in:
- `CLAUDE.md` — project conventions, persistence layout, two-step submit
rationale, MCP arg pattern, MCP session affinity caveat.
- `docs/superpowers/plans/2026-06-24-spark-executor-mcp.md` — the
original implementation plan with Stage 1 / 2 / 3 breakdown.
- `spark_executor/server.py` — the route + exception-handler layer.
- `spark_executor/tools/requests.py` — the Pydantic body models; useful
to read when the MCP 422 response confuses you.
- `spark_executor/core/job_store.py` — Job persistence; explains the
dual-ID contract and the file-backed JSON layout.