David Front

Posted on Aug 10

Bridging Data Quality and Autonomous AI Agents: Introducing gx-mcp-server

#ai #mcp #data #datascience

Author’s Note
I’m David Front - coding since the 1980s, from scientific computing to global web services. Today, I’m happy to shift focus to coding by coordinating the work of AI agents. gx-mcp-server is my first open-source contribution.

1. Introduction

AI agents excel at language but lack built‑in deterministic data validation. Great Expectations (GE) python package provides battle‑tested data-quality checks, but typically via scripts or batch jobs. gx-mcp-server changes that by exposing GE as an MCP‑compliant service, letting agents load data, define rules, run validations, and interpret results programmatically.

You’ll learn:

Why autonomous data validation matters for AI workflows
How MCP standardizes agent–tool interactions
gx-mcp-server’s core design and key features
A walkthrough and an automated test script
Deployment tips, metrics scraping, and next steps

2. Motivation & Challenge

AI pipelines ingest real-world CSVs, databases, and logs. Bad data—nulls, malformed fields, drift—can break downstream tasks. Traditional GE workflows (write suites, schedule jobs, review reports) aren’t realtime or agent‑friendly. Agents need an API: “Is this data valid?” gx-mcp-server fills that gap.

3. Model Context Protocol (MCP)

MCP is an open JSON‑RPC standard for AI agents to discover and call external tools securely. By speaking MCP, gx-mcp-server integrates with any MCP‑aware client—Claude, custom LLM orchestrators, or future platforms—without custom adapters.

4. Architecture Overview

Below is a breakdown of the components and data flow:

AI Agent: Initiates JSON‑RPC calls over MCP/HTTP, sending requests like load_dataset, create_suite, etc.
Protocol Handler (FastMCP): Parses incoming MCP payloads and routes them to the appropriate tool implementation.
HTTP Layer (FastAPI): Exposes /mcp/ endpoints, enforces CORS, origin checks, and rate limits.
Core (gx-mcp-server): Translates MCP calls into Great Expectations API operations—loading data sources, profiling, running checkpoints.
Great Expectations: Executes the actual validation logic, returns structured results.
Storage: In-memory or SQLite-backed handles for datasets and validation runs.
Security (Auth): Basic Auth or JWT/Bearer token validation layer.
Observability: Prometheus metrics (/metrics) and OpenTelemetry spans for tracing.

5. Key Features

Flexible Sources: Inline/local/URL CSV (up to 1 GB), Snowflake, BigQuery.
Dynamic Suites: Create/update expectation suites; optional GE profiler.
Sync & Async: Quick checks or background jobs.
Security: Basic and Bearer‑token auth, CORS, rate limits.
Observability: Prometheus scraping and OpenTelemetry spans.
Transports: HTTP, STDIO, Inspector.

Each choice balances ease‑of‑use with production readiness.

6. Quick Start

Docker

docker run -d -p 8000:8000 --name gx-mcp-server davidf9999/gx-mcp-server:latest

Health

curl http://localhost:8000/mcp/health
# {"status":"ok"}

7. Walkthrough & Demo Script

For readers who want a hands‑on check, the repo includes a fully‑automated smoke test:

scripts/gx_request_response_demo.py – spins up gx‑mcp‑server, loads a demo CSV, runs a checkpoint, prints the results, then shuts the server down.
# clone & run
git clone https://github.com/davidf9999/gx-mcp-server && cd gx-mcp-server
python scripts/gx_request_response_demo.py

What the server sees

Below are two concise MCP exchanges so you can visualize the round‑trip:

1️⃣ load_dataset – inline CSV (two rows)

// request
{
  "method": "tools/call",
  "params": {
    "name": "load_dataset",
    "arguments": {
      "source_type": "inline",
      "source": "id,age\\n1,25\\n2,19"
    }
  }
}

// response (truncated)
{
  "result": {
    "handle": "dset‑1234…",
    "rows": 2,
    "columns": ["id", "age"]
  }
}

2️⃣ run_checkpoint – validate age ∈ 21-65, returns a failure for row 2

// request (after expectation has been added)
{
  "method": "tools/call",
  "params": {
    "name": "run_checkpoint",
    "arguments": {
      "dataset_handle": "dset‑1234…",
      "suite_name": "age_range_suite"
    }
  }
}

// response (excerpt)
{
  "result": {
    "success_percent": 50.0,
    "failing_rows": [
      {"id": 2, "age": 19}
    ]
  }
}

Calling from an LLM

(Claude CLI)

Once the server is running you can let Claude do the same work:

# register the server once / Point your agent to `http://localhost:8000/mcp/`:
claude mcp add gx http://localhost:8000/mcp/
# After this one‑liner, any Claude conversation can invoke gx‑mcp‑server tools automatically.

# natural‑language request • Claude assembles the MCP calls for you
claude "Load CSV id,age
1,25
2,19 and validate age 21‑65; show failed rows"

Claude will send the load_dataset, create an expectation on age, run a checkpoint, and reply with the failed rows—no extra code needed.

(Any MCP‑aware agent follows the same pattern.)

8. Deployment, Metrics & Maintenance

Prometheus scraping: in prometheus.yml:

  scrape_configs:
    - job_name: gx-mcp-server
      static_configs:
        - targets: ['localhost:8000']
      metrics_path: '/metrics'
      scheme: 'http'

Docker: pin tags, mount config.yaml for auth and metrics.
CI/CD: GitHub Actions build & push.
Tracing: --trace with OTLP exporter for Jaeger/Zipkin.

9. Future Work & Roadmap

REST APIs: load JSON from endpoints for validation (e.g., load_dataset('http://api/data')).
Predictive Quality Monitoring: use historical metrics to detect drift and trigger alerts.
Auto‑Generated Expectations: GE’s profiler offers basic generation; next up: AI‑driven rule suggestions.
Kubeflow Hooks: integrate as pipeline steps or Argo tasks in MLOps workflows.

10. Conclusion & Call to Action

gx‑mcp‑server bridges enterprise‑grade data validation and autonomous AI agents. By wrapping Great Expectations in an MCP‑compliant service, you can embed data‑quality checks directly inside your LLM workflows—no bespoke glue code required.

Connect with me:\
• LinkedIn: https://www.linkedin.com/in/david--front/\
• GitHub: https://github.com/davidf9999\
• Email: dfront@gmail.com

Ready to try it?

• GitHub → https://github.com/davidf9999/gx-mcp-server\
• PyPI → pip install gx-mcp-server
• Docker → `docker run -d -p 8000:8000 --name gx-mcp-server davidf9999

🌟 Star the repo, open issues, or submit PRs—community feedback drives the roadmap!

💬 Questions? Leave a comment below or reach out on LinkedIn—I’d love to hear your feedback!

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