Closes the review-and-edit loop for LLM-generated PySpark code:
generate_job_file(code=...) -> {script_path}
read_job_file(script_path=...) -> {content, path, size}
update_job_file(path, content) -> {path, bytes_written}
prepare_submit_job(path) -> {pending_id, ...}
Or, in a single edit cycle:
1. generate (LLM writes initial draft)
2. read (LLM or human inspects)
3. update (overwrite with edited version)
4. prepare (submit for two-step confirmation)
Safety:
- read_job_file has no path restriction (read-only; useful for
inspecting any file the agent can see: scripts, logs/, hadoop-conf/)
- update_job_file is sandboxed to settings.jobs_dir (the same dir
generate_job_file writes to). Rejects paths outside that tree,
including ../-traversal attempts. This protects host-mounted
configs (/etc/passwd, hadoop-conf/*) from being overwritten by
the agent.
- 1 MB cap on both reads and writes so MCP responses stay bounded.
Pydantic body models (ReadJobFileRequest, UpdateJobFileRequest) follow
the Stage 1 pattern so tools/call roundtrips long code strings without
the FastAPI query-length 422.
Tests (13 new):
- 9 unit tests: read success/missing/empty/dir, update success/outside/
relative-escape/missing/oversize/1mb+1, full edit cycle round-trip
- 4 integration tests: read via MCP, missing file 400, write+readback
via MCP, outside-jobs_dir rejection via MCP
163/146 still pass. Live verified end-to-end: generate -> read v1
-> update -> read v2; update /etc/passwd correctly 400'd with
'script_path must be under ... data/jobs/'.
Claude
35bff11edf