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Apache Hive MCP Server

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Apache Hive MCP Server

An MCP server for Apache Hive (HiveServer2), built with FastMCP. Connects to any HiveServer2-compatible endpoint — open-source Apache Hive, Cloudera CDP Base, or Cloudera CDP Public Cloud (CDW) — and exposes two read-only tools that an LLM agent can call.

Project summary

  • What it is — a Model Context Protocol server that lets an LLM agent query Apache Hive: list tables and run read-only SQL, with results returned as JSON.

  • Feature parity — mirrors Cloudera's Impala/Iceberg MCP server: same execute_query + get_schema tools and structure, with the connection layer swapped to HiveServer2 via impyla.

  • Works everywhere HiveServer2 does — open-source Apache Hive, CDP Base (Kerberos/LDAP), and CDP Public Cloud / CDW (Knox, LDAP over HTTPS). Auth, transport, and TLS are all driven by HIVE_* environment variables.

  • Safe by default — execute_query enforces a read-only prefix guard (SELECT/SHOW/DESCRIBE/WITH); write/DDL statements are rejected before a connection is opened.

  • Transport — stdio (default), http, or sse, selected via MCP_TRANSPORT.

  • Tech stack — Python ≥3.10, FastMCP, impyla, uv. 11 unit tests (connection mocked — no live Hive required).

Related MCP server: Hue MCP Server

Tools

Tool

Signature

Description

execute_query

execute_query(query: str) -> str

Execute a read-only SQL query (SELECT, SHOW, DESCRIBE, WITH) and return results as a JSON array of column-keyed objects. Write operations are rejected with an error string.

get_schema

get_schema() -> str

Run SHOW TABLES against the configured database and return the table list as a JSON array of strings.

Configuration

All configuration is via environment variables (or a .env file at the project root).

Variable

Default

Description

HIVE_HOST

localhost

HiveServer2 hostname or IP

HIVE_PORT

10000

HiveServer2 thrift port

HIVE_DATABASE

default

Database to connect to

HIVE_USER

(empty)

Username (leave empty for NOSASL/Kerberos)

HIVE_PASSWORD

(empty)

Password (used with PLAIN/LDAP)

HIVE_AUTH_MECHANISM

PLAIN

Auth method: NOSASL, PLAIN, LDAP, GSSAPI

HIVE_USE_HTTP_TRANSPORT

false

Use HTTP transport instead of binary thrift

HIVE_HTTP_PATH

cliservice

HTTP path when HIVE_USE_HTTP_TRANSPORT=true

HIVE_USE_SSL

false

Enable TLS for the thrift connection

HIVE_KERBEROS_SERVICE_NAME

hive

Kerberos service principal name (GSSAPI only)

MCP_TRANSPORT

stdio

MCP transport: stdio, http, or sse

Copy .env.example to .env and fill in your values.

Deployment presets

Local / development (Docker HiveServer2)

HIVE_HOST=localhost
HIVE_PORT=10000
HIVE_DATABASE=default
HIVE_AUTH_MECHANISM=NOSASL
MCP_TRANSPORT=stdio

CDP Public Cloud (CDW Virtual Warehouse)

HIVE_HOST=<coordinator-hostname>.dw.cloudera.site
HIVE_PORT=443
HIVE_DATABASE=default
HIVE_USER=<workload-username>
HIVE_PASSWORD=<workload-password>
HIVE_AUTH_MECHANISM=LDAP
HIVE_USE_HTTP_TRANSPORT=true
HIVE_HTTP_PATH=cliservice
HIVE_USE_SSL=true
MCP_TRANSPORT=stdio

CDP Base / on-premises with Kerberos

HIVE_HOST=<hiveserver2-host.example.com>
HIVE_PORT=10000
HIVE_DATABASE=default
HIVE_AUTH_MECHANISM=GSSAPI
HIVE_KERBEROS_SERVICE_NAME=hive
MCP_TRANSPORT=stdio

Obtain a Kerberos ticket (kinit) before starting the server.

Running

# Install
pip install -e .
# or with uv:
uv sync

# Copy and edit config
cp .env.example .env

# Start (stdio transport, for use with an MCP host)
uv run hive-mcp-server

MCP client configuration

MCP hosts (Claude Desktop, Cloudera AI Agent Studio, etc.) register servers with a mcpServers JSON block. uvx runs this server straight from GitHub — no local install or PyPI publish required. Fill in the HIVE_* values for your environment (see Configuration and the presets above).

{
    "mcpServers": {
        "Hive": {
            "command": "uvx",
            "args": [
                "--from",
                "git+https://github.com/avelloal/hive-mcp-server@v0.1.0",
                "hive-mcp-server"
            ],
            "env": {
                "HIVE_HOST": "<coordinator-hostname>.dw.cloudera.site",
                "HIVE_PORT": "443",
                "HIVE_DATABASE": "default",
                "HIVE_USER": "<workload-username>",
                "HIVE_PASSWORD": "<workload-password>",
                "HIVE_AUTH_MECHANISM": "LDAP",
                "HIVE_USE_HTTP_TRANSPORT": "true",
                "HIVE_HTTP_PATH": "cliservice",
                "HIVE_USE_SSL": "true"
            }
        }
    }
}

The example above targets CDP Public Cloud / CDW. For local or Kerberos targets, swap the env values using the presets above. @v0.1.0 pins a fixed release — drop it to track the latest, or bump it for a newer version.

For Cloudera AI Agent Studio specifically (registration steps, env-var handling, stdio/uvx limitations), see examples/agent-studio/.

Smoke test

  1. Start a local HiveServer2 (e.g., Apache Hive Docker image):

    docker run -d -p 10000:10000 apache/hive:3.1.3

  2. Configure .env with the local preset above.

  3. Start the server:

    uv run hive-mcp-server

  4. In a second terminal, use the fastmcp dev inspector or any MCP client to call both tools:

    • get_schema() — should return [] or a list of table names.

    • execute_query("SHOW DATABASES") — should return a JSON array of databases.

License

Apache License 2.0 — see LICENSE.

Available Tools

2 tools
execute_queryA

Execute a read-only SQL query on Hive and return results as JSON.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations provided, the description carries the full behavioral burden. It usefully discloses that the tool is read-only and returns JSON, which are meaningful safety and output traits. However, it doesn't explain error behavior, query size limits, permission expectations, or how results are shaped.

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?

A single sentence that front-loads the action, target, and output format. There is no wasted or redundant phrasing; every clause adds information.

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 single-parameter tool with an output schema present, the description covers the essential usage context: the action, the data source, the read-only safety property, and the return format. It could add routing to get_schema, but the core invocation context is adequate.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must carry parameter semantics. 'SQL query on Hive' clarifies that the query parameter should be a Hive SQL string, which adds meaning beyond the schema's bare 'query' field. But the description never mentions the parameter by name or provides syntax, limits, or examples.

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 states a specific verb ('Execute') and resource ('SQL query on Hive'), and also names the output format ('results as JSON'). It clearly distinguishes this tool from the sibling get_schema, which is about schema retrieval rather than running queries.

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?

There is no explicit guidance about when to use this tool versus get_schema, or when not to use it. The 'read-only' qualifier is a constraint, but it doesn't describe alternatives or usage scenarios beyond what the description literally states.

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

get_schemaA

List the table names in the current Hive database.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations, the description carries the disclosure burden. It communicates a read-only, list-style behavior and the scope to the current Hive database, but it does not mention return format, potential limitations, or side effects. For such a simple zero-parameter read operation, this is adequate but not richly transparent.

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?

A single-front-loaded sentence communicates the core action and target resource with no superfluous words. Every part of the description adds value.

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

Completeness5/5

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

The tool has no parameters and an output schema is provided, so the description only needs to specify what is listed and in what context. The single sentence fully covers that for a minimal introspection operation.

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?

The parameter count is 0, so there is no semantic burden on the description. The description accurately describes what the tool will produce, and no parameter-related information is required beyond the schema's empty object.

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 uses a specific verb ('List'), identifies the exact resource ('table names'), and scopes it to the 'current Hive database.' This clearly distinguishes it from the sibling tool execute_query, which focuses on running queries rather than returning schema metadata.

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 makes the tool's purpose obvious: use it when you need the list of table names in the current Hive database. However, it does not explicitly contrast this with execute_query or state when to prefer one over the other, leaving the routing decision somewhat implied.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv0.1.0
    • First observedexecute_query
    • First observedget_schema
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TDQS

A3.9/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: one executes SQL queries, the other lists table names. There is no overlap or ambiguity between them.

Naming Consistency5/5

Both tool names follow a consistent snake_case verb_noun pattern: execute_query and get_schema. The naming is predictable and easy to extend.

Tool Count3/5

Two tools is on the thin side for a full Apache Hive server, though the set is minimal and comprehensible. It feels borderline but not absurdly incomplete.

Completeness3/5

The set covers basic querying and table listing, but lacks explicit schema/column detail, database selection, and non-query operations. An agent can partially work around this via execute_query, but there are notable gaps.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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