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One Python Iceberg MCP server can be configured for multiple REST catalogs, but each catalog still needs its own endpoint, authentication, and—if you want to scan table data—working object-storage access. In a September 2026 test of a specific project, four read-only tools worked against six catalog environments; Databricks Unity Catalog was listed as a seventh path but was not tested.
What the server does
The project, lakehouse-iceberg-2026, exposes four read-only MCP tools:
iceberg_list_tableslists tables.iceberg_describe_tablereturns table details.iceberg_count_rowsreports a row count.iceberg_scan_tablereads sample data and reports scan results.
The same server code selects a catalog through configuration. That is reuse of one implementation, not a universal connection recipe: authentication and storage access differ among services.
What it takes to connect to each catalog
The author’s report describes these configurations and test outcomes. They are observations of the tested setups, not permanent vendor requirements; consult each provider’s current documentation before deploying.
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| Catalog | Reported authentication or configuration | Scan and storage detail | Reported status |
|---|---|---|---|
| Apache Polaris | OAuth2 client ID and secret | Local file storage in the described setup | Tested locally; catalog version 1.7.0 |
| Google BigLake | gcloud login/token; project header in the described setup |
gs:// file access |
Tested |
| Microsoft OneLake | Azure CLI login | adlfs adapter and abfss:// access |
Tested |
| AWS Glue | AWS login with SigV4 | botocore[crt] noted for the AWS login path; s3:// access |
Tested in us-east-1 |
| Amazon S3 Tables | AWS login with SigV4 | s3fs and botocore[crt]; catalog-issued storage credentials |
Tested |
| Snowflake Horizon | Snowflake key-pair JWT | Catalog-issued storage credentials described | Tested |
| Databricks Unity Catalog | No successful setup reported | Not established | Not run: the author’s trial account had ended |
These rows describe the author’s project configurations, not a claim that the services require no other settings or that the same configuration remains current.
Why metadata access is different from scanning
In the reported test, listing, describing, and counting read catalog metadata and worked with PyIceberg alone. A scan also opens table data files. The client therefore needs storage permissions and a compatible file-system adapter where required. A successful REST catalog connection does not, by itself, prove that the client can read the underlying files.
Rank #2
The report identifies adlfs for OneLake and s3fs for S3 Tables, and notes botocore[crt] for the AWS login setup. It also says S3 Tables used a vended-credentials request header, while Horizon returned a storage credential without that request. Those are details of the tested configurations, not universal rules for every account or deployment.
What the September 2026 test establishes
The author reports that all four tools returned without error on the six exercised catalog environments. The sweep measured one run per catalog on September 18 and 19, 2026 (UTC). It used iceberg_mcp.py 1.0.0, PyIceberg 0.12.0, PyArrow 25.0.1, Python 3.14.7, and the listed storage and authentication packages. Polaris was 1.7.0; managed catalog versions were not reported. The report is a bounded project test, not an independent compatibility certification or a repeatable benchmark.
Rank #3
- Used Book in Good Condition
The report includes conflicting timings for a three-row OneLake scan. Its narrative gives 858.5 seconds with DefaultAzureCredential and 3.4 seconds with AzureCliCredential; its summary instead gives 553.1 seconds and 0.6 seconds, respectively. Because the same report presents both pairs, neither should be treated as a definitive performance result. The author also provides single-call timings for other catalog operations and cautions that they show scale only. Different tables and row counts make those calls unsuitable for like-for-like performance comparisons.
How to assess a catalog for your use case
Start by separating metadata needs from data-file reads. If you only need to enumerate tables, inspect schemas, or request counts, the storage adapter may not be part of that operation in the tested project. If you need sample rows or scans, verify the object-store credential flow and adapter as well as the catalog login.
Rank #4
- Confirm the catalog endpoint and its current authentication method.
- Determine where the table’s data files live and which identity can read them.
- Check whether your Python client needs a storage adapter such as
adlfsors3fs. - Test metadata operations and a real file scan separately; success at the catalog layer does not validate storage access.
- Treat Unity Catalog as an untested path in this report rather than inferring compatibility from the six exercised environments.
The reported project and test assets are available in the project repository; the report appeared on DEV Community on September 20, 2026.
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