Iceberg MCP Server (via Impala)
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Alternatives to Iceberg MCP Server (via Impala)
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Related Servers
- AlicenseBqualityDmaintenanceEnables read-only access to Apache Iceberg tables via Impala, allowing LLMs to inspect database schemas and execute SQL queries to retrieve data from Iceberg tables.213Apache 2.0
- AlicenseNot gradedqualityBmaintenanceEnables AI agents and MCP hosts to query Trino while carrying each end user's verified OIDC identity, so row filters, column masks, and catalog scope are enforced by the existing policy engine rather than a shared service account. Queries are read-only by construction, with quotas, revocation, and audit logging.348 PyPI7Apache 2.0
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to audit data governance, check access control roles, query namespaces, and inspect Iceberg metadata.1MIT
- AlicenseNot gradedqualityCmaintenanceEnables read-only Spark SQL queries against Apache Iceberg tables, allowing LLM clients to inspect schemas and run SELECT-style queries on HDFS/S3 data without Hive or Impala.Apache 2.0
- AlicenseAqualityCmaintenanceProvides read-only SQL access to Apache Iceberg tables via HiveServer2, enabling querying, schema discovery, and database listing on Cloudera Data Platform.31Apache 2.0
- AlicenseNot gradedqualityDmaintenanceProvides read-only access to Iceberg tables via Apache Impala, enabling LLMs to inspect database schemas and execute SQL queries on Iceberg data.Apache 2.0
TDQS
Scored across 2 tools
The two tools occupy clearly separate roles: `get_schema` for metadata discovery and `execute_query` for running read-only SQL. There is no realistic scenario where an agent would confuse one for the other, and the descriptions even specify the intended workflow order (schema first, then query).
Both names follow a strict verb_noun snake_case pattern (`execute_query`, `get_schema`), using consistent verbs and clear object nouns. No mixing of conventions or casing styles.
Two tools is on the thin side relative to the typical 3-15 range, but for a deliberately read-only SQL access layer both tools earn their place and form a complete minimal workflow. It is slightly under-provisioned but not wasteful or confusing.
The surface covers the core read-only lifecycle: inspect schema, then query. Minor conveniences (e.g. dedicated list-databases, table description, or EXPLAIN helper) are absent, but an agent can reach equivalent results by issuing SHOW/DESCRIBE through `execute_query`, so no hard dead ends exist. Write operations are intentionally excluded.