What can you do with this server?

This server provides read-only and branch-based access to Apache Iceberg tables via HiveServer2, enabling SQL querying, schema exploration, and Iceberg branch/snapshot management. * Execute read-only queries: Run SELECT, SHOW, DESCRIBE, WITH, and EXPLAIN SQL statements on Hive/Iceberg tables. * List tables: Retrieve table names from the configured or a specified Hive database. * List databases: Enumerate all Hive databases visible to the connected user. * List Iceberg snapshots: View snapshot history for a table. * List Iceberg refs: Inspect existing branches and tags on a table. * Create Iceberg branches: Fork a branch from the current state, a specific snapshot ID, or a timestamp. * Drop Iceberg branches: Remove an existing branch. * Fast-forward Iceberg branches: Advance a branch along its hierarchy. * Query Iceberg branches: Read data from a specific branch. * Execute DML on branches: Perform INSERT, UPDATE, or DELETE operations on a branch.

Which integrations are available for this server?

Provides read-only access to Iceberg tables on Cloudera Data Platform (CDP) via Apache Hive (HiveServer2). Allows querying Iceberg tables on Cloudera Data Platform (CDP) using Hive.

How do I use iceberg-mcp-server-hive?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@iceberg-mcp-server-hive list all databases" 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.

iceberg-mcp-server-hive

by frothkoetter

Overview Schema Related Servers Score Discussions

Python

Remote

Cloudera Iceberg MCP Server (via Hive)

Fork of cloudera/iceberg-mcp-server that uses Apache Hive (HiveServer2) instead of Impala for read-only access to Iceberg tables on CDP.

MCP Tools

Tool	Description
`execute_query(query)`	Run read-only SQL (`SELECT`, `SHOW`, `DESCRIBE`, `WITH`, `EXPLAIN`)
`get_schema(database?)`	List tables in the configured or given database
`list_databases()`	List all visible Hive databases
`list_iceberg_snapshots(database, table)`	Snapshot history (`db.table.HISTORY`, with TBLPROPERTIES fallback)
`list_iceberg_refs(database, table)`	Branches and tags (`db.table.REFS`)
`create_iceberg_branch(...)`	Create branch from current state, snapshot ID, or timestamp
`drop_iceberg_branch(...)`	Drop a branch
`fast_forward_iceberg_branch(...)`	Fast-forward branch hierarchy
`query_iceberg_branch(...)`	Read from `db.table.branch_<name>`
`execute_iceberg_branch_dml(...)`	`INSERT` / `UPDATE` / `DELETE` on a branch

Iceberg branching (and tagging) is supported in Hive on CDP, not Impala. See branching and tagging.

Audit / write branch workflow

list_iceberg_snapshots — pick a snapshot ID or timestamp
list_iceberg_refs — inspect existing branches/tags
create_iceberg_branch — fork an audit branch (FOR SYSTEM_VERSION or current head)
query_iceberg_branch — read branch state
execute_iceberg_branch_dml — write changes on the branch only
fast_forward_iceberg_branch — advance a branch when ready
drop_iceberg_branch — cleanup

Branch refs use lowercase branch_ prefix: mydb.mytable.branch_audit.

Related MCP server: MCP Trino Server

Configuration

Connection uses impyla against HiveServer2 (HTTP transport for CDP/Knox).

Example JDBC URL from CDP Data Warehouse:

jdbc:hive2://hs2-cdw-aw-se-hive.dw-se-sandbox-aws.a465-9q4k.cloudera.site/default;transportMode=http;httpPath=cliservice;ssl=true

Maps to MCP env vars:

JDBC / CDP	Env var
Host in URL	`HIVE_HOST`
Path after host (`/default`)	`HIVE_DATABASE`
`httpPath=cliservice`	`HIVE_HTTP_PATH`
`transportMode=http`	`HIVE_USE_HTTP_TRANSPORT=true`
`ssl=true`	`HIVE_USE_SSL=true`
Port (443 implied)	`HIVE_PORT=443`
LDAP user/password	`HIVE_USER`, `HIVE_PASSWORD`

Variable	Default	Description
`HIVE_HOST`	—	HiveServer2 or Knox gateway host
`HIVE_PORT`	`443`	HS2 port (443 for Knox HTTP)
`HIVE_USER`	—	LDAP / service user
`HIVE_PASSWORD`	—	Password
`HIVE_DATABASE`	`default`	Default database for `SHOW TABLES`
`HIVE_AUTH_MECHANISM`	`LDAP`	impyla auth mechanism
`HIVE_USE_HTTP_TRANSPORT`	`true`	HTTP transport (typical on CDP)
`HIVE_HTTP_PATH`	`cliservice`	Knox / HS2 HTTP path
`HIVE_USE_SSL`	`true`	TLS
`MCP_TRANSPORT`	`stdio`	`stdio`, `http`, or `sse`

Claude Desktop / Agent Studio

Cloudera Agent Studio (recommended)

Agent Studio only supports stdio MCP servers launched with uvx (Python) or npx (Node.js). Use a git URL so the runtime can install the package; do not use uv run unless the repo is checked out on the same machine.

{
  "mcpServers": {
    "iceberg-mcp-server-hive": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/frothkoetter/iceberg-mcp-server-hive@main",
        "run-server"
      ],
      "env": {
        "HIVE_HOST": "hs2-cdw-aw-se-hive.dw-se-sandbox-aws.a465-9q4k.cloudera.site",
        "HIVE_PORT": "443",
        "HIVE_USER": "YOUR_USER",
        "HIVE_PASSWORD": "YOUR_PASSWORD",
        "HIVE_DATABASE": "default",
        "HIVE_USE_HTTP_TRANSPORT": "true",
        "HIVE_HTTP_PATH": "cliservice",
        "HIVE_USE_SSL": "true",
        "HIVE_AUTH_MECHANISM": "LDAP"
      }
    }
  }
}

Registration tips

Use placeholder credentials during catalog registration; provide real HIVE_USER / HIVE_PASSWORD when attaching the MCP server to a workflow agent.
If you see "We could not figure out the tools offered by the MCP server", the server may still work in workflows — Agent Studio documents occasional tool-discovery failures. Add the MCP server to your agent manually and select tools there.
Avoid writing to stdout from wrapper scripts — stdio transport uses stdout for JSON-RPC. This server logs only through the MCP SDK.
Ensure the Agent Studio environment can reach HIVE_HOST on port 443 (Knox / Hive VW).

Troubleshooting MCP server load failures

If CrewAI Agent Studio, Cursor, or another MCP host fails to connect to this server via uvx, check the following.

`FastMCP` / `mcp` import error

uvx installs the latest mcp package (currently 2.x). MCP Python SDK 2.0 renamed FastMCP to MCPServer. Older server builds crash on startup with:

ImportError: cannot import name 'FastMCP' from 'mcp.server'

Use a server build that includes the dual-import fix (MCPServer + FastMCP fallback), or pin mcp>=1.28,<2 in your environment.

Verify locally:

HIVE_HOST="hs2.example.cloudera.site" \
HIVE_USER="test" HIVE_PASSWORD="test" \
uv run python scripts/test_mcp_stdio.py uvx --from . run-server

Expected output: OK initialize and OK tools/list: 10 tools.

Ensure uvx is on the Agent Studio host PATH (which uvx).

Claude Desktop (local checkout)

{
  "mcpServers": {
    "iceberg-mcp-server-hive": {
      "command": "uv",
      "args": ["run", "run-server"],
      "env": {
        "HIVE_HOST": "hs2-your-cluster.example.cloudera.site",
        "HIVE_PORT": "443",
        "HIVE_USER": "username",
        "HIVE_PASSWORD": "password",
        "HIVE_DATABASE": "default"
      }
    }
  }
}

Local development

git clone https://github.com/<your-org>/iceberg-mcp-server-hive.git
cd iceberg-mcp-server-hive
uv sync --dev
export HIVE_HOST=... HIVE_USER=... HIVE_PASSWORD=...
uv run run-server

Differences from upstream (Impala)

Environment variables use HIVE_* instead of IMPALA_*
get_schema returns {database, tables} and accepts an optional database name
Added list_databases tool
execute_query returns {columns, rows} for SELECT results

Examples

See ./examples for LangChain and OpenAI SDK notebooks (update env vars from IMPALA_* to HIVE_*).

Available Tools

3 tools

execute_queryA

Execute a read-only SQL query on Hive (Iceberg tables) and return JSON results.

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes

TDQS

A3.5/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It states the tool is read-only, which is a key behavioral trait, but lacks details on error handling, performance, or limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no wasted words. However, it could include more context without becoming verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple tool (one param, no output schema, no annotations), the description covers basic purpose but omits details like result structure, pagination, or error behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% coverage, so the description should compensate. It adds that the query is SQL, but provides no additional details like syntax, formatting, or restrictions, leaving agents underinformed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the action (execute), resource (SQL query on Hive Iceberg tables), and output (JSON results). It effectively distinguishes from sibling tools like get_schema and list_databases.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for executing read-only SQL queries but does not provide explicit guidance on when to use it versus siblings, nor does it mention exclusions or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_schemaA

List table names in the configured Hive database, or in an optional database.

ParametersJSON Schema

Name	Required	Description	Default
`database`	No

TDQS

A3.7/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It mentions listing tables but does not disclose permissions needed, error behavior if database doesn't exist, or whether the output includes fully qualified names. The read-only nature is implied but not explicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, front-loaded with the verb 'List' and resource 'table names'. No unnecessary words; efficient and to the point.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool is simple with one optional parameter and no output schema, the description is adequate but could be more complete by mentioning what exactly is returned (e.g., table names only, not columns) and the default database behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description adds value by explaining that the database parameter is optional and lists table names from the configured or specified database. It clarifies the parameter's purpose beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists table names in a Hive database, with an optional parameter to specify a database. It distinguishes itself from siblings like list_databases (which lists databases) and execute_query (which runs queries).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for listing tables but does not explicitly state when to use this tool versus alternatives like list_databases or execute_query. No exclusions or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_databasesA

List all Hive databases visible to the connected user.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A3.6/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided; description only adds 'visible to the connected user' but does not disclose other behavioral traits like limitations, side effects, or performance.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, front-loaded with action and resource, no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple, parameterless tool, the description covers core functionality and important qualifier; lacks output details but still adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 0 parameters, so baseline is 4; description adds meaning by specifying scope ('visible to the connected user'), which clarifies the result set.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'List' and the resource 'Hive databases', and distinguishes from siblings (execute_query, get_schema) by specifying a listing operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives; lacks context such as prerequisite or typical use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

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TDQS

A3.8/5.0

Disambiguation5/5

Each tool targets a distinct function: executing queries, listing tables, and listing databases. There is no overlap in purpose.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using snake_case, making them predictable and easy to understand.

Tool Count4/5

With only three tools, the server is minimal but covers the essential read-only operations for Hive databases. The count is reasonable given the focused scope.

Completeness3/5

The server lacks a tool for retrieving detailed table schema or previewing data, which are common needs when exploring databases. This is a notable gap for a read-only explorer.

Maintenance

ActivityMaintained

ResponsivenessSyncing

Resources

GitHub Repository

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Cloudera Iceberg MCP Server (via Hive)

MCP Tools

Audit / write branch workflow

Configuration

Claude Desktop / Agent Studio

Cloudera Agent Studio (recommended)

Troubleshooting MCP server load failures

FastMCP / mcp import error

Claude Desktop (local checkout)

Local development

Differences from upstream (Impala)

Examples

Available Tools

TDQS

TDQS

TDQS

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

MCP Iceberg Catalog

MCP Trino Server

Cloudera Iceberg MCP Serverofficial

Cloudera Iceberg MCP Server

Latest Blog Posts

MCP directory API

`FastMCP` / `mcp` import error