oracle-eim-dq
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@oracle-eim-dqList the active EIM data-quality rules and show the Markdown report."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Oracle EIM Data Quality
Standalone MCP server for governed EIM data-quality checks against On-Prem Oracle and Oracle ATP. This is not the Oracle MCP chatbot.
The chatbot project (oracle-mcp-chatbot) answers natural-language questions.
This project loads ACTIVE rules from EIM_APPS.EIM_DQ_RULES_LOOKUP, runs
approved read-only SQL, and returns a director-ready Markdown report. An
optional isolated writer can atomically persist governed summaries and failed
record details without enabling arbitrary DML.
pip install --target .pydeps -r requirements-dev.txt
PYTHONPATH=.pydeps:src pytest
cp .env.example .env # add credentials
PYTHONPATH=.pydeps:src python -m oracle_mcp.server --profile both --check
PYTHONPATH=.pydeps:src python -m oracle_mcp.server --profile bothRule workflow and report contract: docs/data-quality-framework.md.
What it does
Capability | How |
Separate from the chatbot | Own repo, own MCP server name |
ACTIVE rules only | Catalog filter |
Checkpoint context |
|
Read-only source access | AST guard plus |
Metrics | Total, failed, pass %, failure %, severity |
Persisted reconciliation | Fixed parameterized INSERTs through a separate writer |
Trend | Like-for-like population signature vs persisted history |
Related MCP server: database-mcp
MCP tools
list_active_dq_rulesexecute_data_quality_rulestart_dq_run,execute_and_persist_data_quality_rule,get_dq_run_report, andemail_dq_run_summary(registered only when persistence is enabled). Onerun_ididentifies the full company report. Mail sends recon summary metrics only; failed-record details are never included.
Enable persistence
Persistence is disabled by default. Have a DBA review and run
sql/04_dq_results_schema.sql, then configure the separate DQ_WRITE_*
credentials in .env and set:
ORACLE_MCP_DQ_PERSISTENCE_ENABLED=trueThe writer receives INSERT only on EIM_APPS.EIM_DQ_RECON_SUMMARY and
EIM_APPS.EIM_DQ_FAILED_RECORDS. It must not reuse the read-only account.
Retention/purge is DBA-owned; the MCP process has no DELETE privilege.
Cursor MCP
Point Cursor at mcp-clients/cursor-mcp.json (replace absolute paths). The
workspace .cursor/mcp.json should register oracle-eim-dq with cwd set to
this repository, not oracle-mcp-chatbot.
Test locally
Run these commands from this repository:
# 1. Unit and security tests (no database required)
PYTHONPATH=.pydeps:src .pydeps/bin/pytest -q
# 2. Verify both Oracle connections
PYTHONPATH=.pydeps:src python3 -m oracle_mcp.server --profile both --check
# 3. Verify the MCP tool surface and read the live ACTIVE-rule catalog
PYTHONPATH=.pydeps:src python3 scripts/verify_dq.pyExpected live verification:
{
"status": "OK",
"server": "oracle-eim-dq",
"databases": ["ATP", "ONPREM"],
"dq_tools": ["execute_data_quality_rule", "list_active_dq_rules"]
}If active_rule_count is 0, connectivity and MCP are working but the governed
catalog has no ACTIVE rules yet. Populate and approve catalog rows outside this
read-only application, then rerun the verification.
After changing .cursor/mcp.json, reload Cursor's MCP servers. In chat, first
ask: List the active EIM data-quality rules. Do not attempt a rule execution
until that call returns an ACTIVE rule ID.
This server cannot be deployed
Maintenance
Related MCP Connectors
Auto-discover validation rules from data — scan, profile, health-score. No rules to write.
CloudOracle - 14-tool multi-cloud compliance MCP: AWS, Azure, GCP posture, IAM, configs.
Governed data discovery, exact queries, decisions, simulations, and runtime utilities over MCP.
Paid remote MCP for governed database query review, SQL simulation, approvals, and audits.
Related MCP Servers
- AlicenseBqualityDmaintenanceAgentic data quality MCP server — runs structured validation rules against warehouses (DuckDB, BigQuery, Athena, Databricks, Postgres), diagnoses failures with LLM root cause analysis, and proposes SQL remediations. Full audit trail of every AI decision.64Apache 2.0
- FlicenseNot gradedqualityDmaintenanceAutomatically discovers database schema, performs data quality checks on tables and columns, and generates natural-language root cause analysis reports using Ollama LLM.-
- FlicenseNot gradedqualityBmaintenanceProvides governed MCP tools for validating, explaining, and remediating clinical supply chain data quality across Bronze, Silver, and Gold layers on Azure Databricks, with data contracts and an evaluation harness.-
- AlicenseAqualityBmaintenanceGenerates and deploys Cloudera Data Engineering (CDE) Airflow jobs from ODCS data contracts, validating live table data in CDW against schema and data-quality rules without WAP staging.13Apache 2.0