042ce34837
* expanded the Workday fixture data in data.py so each mock worker now carries the paired source attributes needed for upcoming drift checks: Workday title, AD title, Workday cost center context, AD department, legal name, preferred name, AD display name, and AD enabled state. Existing fields used by the current tools were preserved, so this was a schema expansion rather than a behavioral change.
* added a dedicated data-layer detector in data.py named scan_status_reconciliation_mismatches(). It scans the current mock worker set and flags the exact mismatch you asked for: status == "Terminated" in Workday while ad_enabled == True in AD. The response shape follows the existing manager-scan pattern with scan_summary plus a mismatches list.
* exposed the status reconciliation detector as a dedicated MCP tool in server.py by importing the new data-layer function and adding scan_status_reconciliation(). This keeps the existing architecture intact: the server remains a thin wrapper, and the scan logic stays in data.py.
* added scan_job_title_mismatches() to data.py. It compares workday_title against ad_title for each worker and returns the same structured pattern as the other detectors: scan_summary plus a mismatches list with both source values, a deterministic mismatch type, and severity.
* exposed the title detector as a dedicated MCP tool in server.py by importing the new data-layer function and adding scan_job_title_drift(). The server is still acting as a thin wrapper over the detector in data.py.
* added scan_department_drift() to data.py. It compares the Workday department context against the AD department string and includes the Workday cost center in each mismatch record so the drift is easier to interpret during reconciliation.
* exposed the department detector as a dedicated MCP tool in server.py by adding scan_department_mismatches(), which delegates to the detector in data.py. The existing tool structure remains unchanged.
* added scan_name_variance() to data.py. The detector normalizes legal, preferred, and AD display names, allows reasonable preferred-name usage, and flags cases where the AD display name no longer aligns to either the legal or preferred first name plus the legal last name.
* exposed the name-variance detector as a dedicated MCP tool in server.py by adding scan_name_variance_mismatches(), which delegates to the detector in data.py. The server still remains a thin wrapper layer.
* I’m checking the Workday project’s current test layout first so I can add one focused test file that matches the existing environment and runner configuration.
Searched for files matching `**/Workday/workday-mcp/**/*`, 3 matches
The Workday runtime is even thinner than the planning docs imply: there’s no test package or local pyproject in that folder yet. I’m checking the parent Workday artifacts and the root repo config so the new tests land in a runnable location.
Read [](file:///c%3A/Users/castn1.CORP/OneDrive%20-%20Wheels/Repos/mcp_servers/Workday/workday-mcp)
Searched for files matching `**/pyproject.toml`, 1 match
Searched for files matching `**/pytest.ini`, no matches
Searched for files matching `**/tox.ini`, no matches
I’ve confirmed there’s no existing Workday test harness, so I’m borrowing the minimal testing conventions from the Identity project before I add one focused detector test file.
Searched for regex `pytest|testpaths|pythonpath` (`**/Identity/pyproject.toml`), 2 results
Read [](file:///c%3A/Users/castn1.CORP/OneDrive%20-%20Wheels/Repos/mcp_servers/Identity/pyproject.toml)
Fetching Python environment information
Completed with input: {
"resourcePath": "c:\\Users\\castn1.CORP\\OneDrive - Wheels\\Repos\\mcp_servers\\Workday\\workday-mcp"
}
I have enough context to add the test file. The Workday virtual environment does not currently include `pytest`, so I’m installing the minimal test dependency now and then I’ll add a single focused test module for the new detectors and wrappers.
Installing pytest>=7.4.0 in Python 3.13.12 (.venv)
Completed with input: {
"resourcePath": "c:\\Users\\castn1.CORP\\OneDrive - Wheels\\Repos\\mcp_servers\\Workday\\workday-mcp",
"packageList": [
"pytest>=7.4.0"
]
}
Completed: *Expose status MCP tool* (11/18)
The test dependency is installed. I’m adding one focused Workday test module now for the four detectors and their MCP wrapper functions.
Generating patch (95 lines) in [](file:///c%3A/Users/castn1.CORP/OneDrive%20-%20Wheels/Repos/mcp_servers/Workday/workday-mcp/tests/test_mismatch_scans.py)
```
```
The test file is in place. I’m running only that focused test module now and checking for any file errors introduced by the new coverage.
Ran terminal command: cd "/c/Users/castn1.CORP/OneDrive - Wheels/Repos/mcp_servers/Workday/workday-mcp" && "c:/Users/castn1.CORP/OneDrive - Wheels/Repos/mcp_servers/Workday/workday-mcp/.venv/Scripts/python.exe" -m pytest tests/test_mismatch_scans.py -q
Checked [](file:///c%3A/Users/castn1.CORP/OneDrive%20-%20Wheels/Repos/mcp_servers/Workday/workday-mcp/tests/test_mismatch_scans.py), [](file:///c%3A/Users/castn1.CORP/OneDrive%20-%20Wheels/Repos/mcp_servers/Workday/workday-mcp/server.py), [](file:///c%3A/Users/castn1.CORP/OneDrive%20-%20Wheels/Repos/mcp_servers/Workday/workday-mcp/lib/data.py), no problems found
[[ PROCESSING: Mode=NORMAL_MODE | Intent=Report completion of the focused test-coverage step and pause before any optional documentation/status update | Strategy=Summarize the new test file and the verification result, then wait for NEXT ]]
Step 10 is complete on `feat/workday-mismatch-expansion`.
I added focused pytest coverage in test_mismatch_scans.py for the four new detectors and their MCP wrappers:
`scan_status_reconciliation_mismatches`, `scan_job_title_mismatches`, `scan_department_drift`, `scan_name_variance`, plus the corresponding server functions in server.py.
Verification passed. Running the focused test module succeeded with `8 passed in 0.49s`, and there are no reported errors in test_mismatch_scans.py, server.py, or data.py.
Reply exactly with:
NEXT
After that, I’ll do the final atomic step: update status or planning documentation only where needed to reflect the newly implemented mismatch coverage, then I’ll close the session with a summary, snapshot draft, and a Conventional Commit suggestion for your branch review flow.
Made changes.
* Updated to reflect changes
---------
Co-authored-by: nathan <nathan@castaldifamily.com>
298 lines
9.9 KiB
Python
298 lines
9.9 KiB
Python
from typing import Any
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# Mock dataset with reporting-line relationships for manager checks (WIS-017 prep)
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MOCK_WORKERS: dict[str, dict[str, Any]] = {
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"EMP001": {
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"name": "Nathan",
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"legal_name": "Nathaniel Cole",
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"preferred_name": "Nathan",
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"ad_display_name": "Nathan Cole",
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"status": "Active",
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"ad_enabled": True,
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"dept": "IT",
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"workday_cost_center": "CC100-IT",
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"workday_title": "Systems Engineer",
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"ad_title": "Systems Engineer",
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"ad_department": "IT",
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"email": "nathan@example.com",
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"manager_id": "EMP010",
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},
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"EMP002": {
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"name": "Terminated User",
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"legal_name": "Taylor Brooks",
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"preferred_name": "Taylor",
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"ad_display_name": "Taylor Brooks",
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"status": "Terminated",
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"ad_enabled": True,
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"dept": "Sales",
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"workday_cost_center": "CC200-SALES",
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"workday_title": "Account Executive",
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"ad_title": "Account Executive",
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"ad_department": "Sales",
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"email": "user2@example.com",
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"manager_id": "EMP020",
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},
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"EMP003": {
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"name": "Alicia",
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"legal_name": "Alicia Gomez",
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"preferred_name": "Alicia",
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"ad_display_name": "Alicia Gomez",
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"status": "Active",
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"ad_enabled": True,
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"dept": "IT",
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"workday_cost_center": "CC100-IT",
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"workday_title": "Senior Systems Analyst",
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"ad_title": "Systems Analyst",
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"ad_department": "IT",
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"email": "alicia@example.com",
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"manager_id": "EMP010",
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},
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"EMP004": {
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"name": "Jordan",
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"legal_name": "Jordan Lee",
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"preferred_name": "Jordan",
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"ad_display_name": "Jordan Lee",
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"status": "Leave",
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"ad_enabled": True,
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"dept": "Finance",
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"workday_cost_center": "CC300-FIN",
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"workday_title": "Finance Analyst",
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"ad_title": "Finance Analyst",
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"ad_department": "Accounting",
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"email": "jordan@example.com",
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"manager_id": "EMP030",
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},
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"EMP010": {
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"name": "Priya Manager",
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"legal_name": "Priya Narayanan",
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"preferred_name": "Priya",
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"ad_display_name": "Priya Manager",
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"status": "Active",
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"ad_enabled": True,
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"dept": "IT",
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"workday_cost_center": "CC110-IT-MGMT",
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"workday_title": "IT Manager",
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"ad_title": "IT Manager",
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"ad_department": "IT",
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"email": "priya@example.com",
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"manager_id": "EMP100",
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},
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"EMP020": {
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"name": "Ramon Director",
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"legal_name": "Ramon Alvarez",
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"preferred_name": "Ramon",
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"ad_display_name": "Ramon Director",
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"status": "Active",
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"ad_enabled": True,
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"dept": "Sales",
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"workday_cost_center": "CC210-SALES-MGMT",
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"workday_title": "Sales Director",
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"ad_title": "Sales Director",
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"ad_department": "Sales",
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"email": "ramon@example.com",
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"manager_id": "EMP100",
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},
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"EMP030": {
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"name": "Morgan Lead",
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"legal_name": "Morgan Patel",
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"preferred_name": "Morgan",
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"ad_display_name": "Morgan Patel",
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"status": "Active",
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"ad_enabled": True,
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"dept": "Finance",
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"workday_cost_center": "CC310-FIN-MGMT",
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"workday_title": "Finance Lead",
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"ad_title": "Finance Lead",
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"ad_department": "Finance",
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"email": "morgan@example.com",
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"manager_id": "EMP100",
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},
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"EMP100": {
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"name": "Chief Exec",
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"legal_name": "Evelyn Carter",
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"preferred_name": "Evelyn",
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"ad_display_name": "Evelyn Carter",
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"status": "Active",
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"ad_enabled": True,
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"dept": "Executive",
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"workday_cost_center": "CC999-EXEC",
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"workday_title": "Chief Executive Officer",
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"ad_title": "Chief Executive Officer",
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"ad_department": "Executive",
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"email": "ceo@example.com",
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"manager_id": "",
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},
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# Intentional unresolved manager reference for mismatch test scenarios
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"EMP777": {
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"name": "Mismatch Case",
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"legal_name": "Alexandra Rivers",
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"preferred_name": "Alex",
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"ad_display_name": "Jordan Rivers",
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"status": "Active",
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"ad_enabled": True,
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"dept": "Operations",
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"workday_cost_center": "CC400-OPS",
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"workday_title": "Operations Specialist",
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"ad_title": "Operations Specialist",
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"ad_department": "Operations",
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"email": "mismatch@example.com",
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"manager_id": "EMP999",
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},
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}
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def scan_status_reconciliation_mismatches() -> dict[str, Any]:
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"""Detect workers terminated in Workday but still enabled in AD."""
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mismatches: list[dict[str, Any]] = []
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total_scanned = 0
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for employee_id, details in MOCK_WORKERS.items():
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total_scanned += 1
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workday_status = details.get("status")
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ad_enabled = bool(details.get("ad_enabled", False))
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if workday_status == "Terminated" and ad_enabled:
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mismatches.append(
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{
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"employee_id": employee_id,
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"employee_name": details["name"],
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"workday_status": workday_status,
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"ad_enabled": ad_enabled,
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"mismatch_type": "terminated_but_enabled",
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"severity": "high",
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}
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)
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return {
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"scan_summary": {
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"total_records_checked": total_scanned,
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"mismatches_found": len(mismatches),
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"status": "action_required" if mismatches else "clean",
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},
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"mismatches": mismatches,
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}
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def scan_job_title_mismatches() -> dict[str, Any]:
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"""Detect workers whose Workday title differs from their AD title."""
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mismatches: list[dict[str, Any]] = []
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total_scanned = 0
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for employee_id, details in MOCK_WORKERS.items():
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total_scanned += 1
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workday_title = details.get("workday_title", "")
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ad_title = details.get("ad_title", "")
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if workday_title and ad_title and workday_title != ad_title:
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mismatches.append(
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{
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"employee_id": employee_id,
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"employee_name": details["name"],
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"workday_title": workday_title,
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"ad_title": ad_title,
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"mismatch_type": "job_title_mismatch",
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"severity": "medium",
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}
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)
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return {
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"scan_summary": {
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"total_records_checked": total_scanned,
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"mismatches_found": len(mismatches),
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"status": "action_required" if mismatches else "clean",
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},
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"mismatches": mismatches,
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}
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def scan_department_drift() -> dict[str, Any]:
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"""Detect workers whose Workday department context differs from AD department."""
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mismatches: list[dict[str, Any]] = []
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total_scanned = 0
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for employee_id, details in MOCK_WORKERS.items():
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total_scanned += 1
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workday_department = details.get("dept", "")
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workday_cost_center = details.get("workday_cost_center", "")
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ad_department = details.get("ad_department", "")
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if workday_department and ad_department and workday_department != ad_department:
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mismatches.append(
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{
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"employee_id": employee_id,
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"employee_name": details["name"],
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"workday_department": workday_department,
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"workday_cost_center": workday_cost_center,
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"ad_department": ad_department,
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"mismatch_type": "department_drift",
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"severity": "medium",
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}
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)
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return {
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"scan_summary": {
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"total_records_checked": total_scanned,
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"mismatches_found": len(mismatches),
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"status": "action_required" if mismatches else "clean",
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},
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"mismatches": mismatches,
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}
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def _normalize_name_tokens(value: str) -> list[str]:
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return [token for token in value.lower().replace(".", " ").split() if token]
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def scan_name_variance() -> dict[str, Any]:
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"""Detect AD display names that do not align to legal or preferred Workday names."""
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mismatches: list[dict[str, Any]] = []
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total_scanned = 0
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for employee_id, details in MOCK_WORKERS.items():
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total_scanned += 1
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legal_name = details.get("legal_name", "")
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preferred_name = details.get("preferred_name", "")
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ad_display_name = details.get("ad_display_name", "")
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if not legal_name or not ad_display_name:
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continue
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legal_tokens = _normalize_name_tokens(legal_name)
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preferred_tokens = _normalize_name_tokens(preferred_name)
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display_tokens = _normalize_name_tokens(ad_display_name)
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if not legal_tokens or not display_tokens:
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continue
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legal_first = legal_tokens[0]
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legal_last = legal_tokens[-1]
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preferred_first = preferred_tokens[0] if preferred_tokens else ""
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display_first = display_tokens[0]
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display_last = display_tokens[-1]
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first_name_aligned = display_first in {legal_first, preferred_first}
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last_name_aligned = display_last == legal_last
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if first_name_aligned and last_name_aligned:
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continue
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mismatches.append(
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{
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"employee_id": employee_id,
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"employee_name": details["name"],
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"workday_legal_name": legal_name,
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"workday_preferred_name": preferred_name,
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"ad_display_name": ad_display_name,
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"mismatch_type": "name_variance_requires_review",
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"severity": "low",
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}
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)
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return {
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"scan_summary": {
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"total_records_checked": total_scanned,
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"mismatches_found": len(mismatches),
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"status": "action_required" if mismatches else "clean",
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},
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"mismatches": mismatches,
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} |