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Apache Hive MCP Server

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Python
Remote

Apache Hive MCP Server

Ein MCP-Server für Apache Hive (HiveServer2), erstellt mit FastMCP. Er verbindet sich mit jedem HiveServer2-kompatiblen Endpunkt – Open-Source Apache Hive, Cloudera CDP Base oder Cloudera CDP PublicCloud (CDW) – und stellt zwei schreibgeschützte Tools bereit, die ein LLM-Agent aufrufen kann.

Projektbersicht

  • Was es ist – ein Model Context Protocol-Server, der es einem LLM-Agenten erlaubt, Apache Hive abzufragen: Tabellen auflisten und schreibgeschützte SQL ausführen, wobei die Ergebnisse als JSON zurückgegeben werden.

  • Feature-Parität – orientiert sich am [Impala/Iceberg MCP-Server] (https://github.com/cloudera/iceberg-mcp-server) – der gleichen execute_query- und get_schema-Tools und -Struktur, mit auf HiveServer2 umgestellter Verbindungsschicht über 4- und/)。 [HIVER] (https://github.com/cloudera/impyla).

  • Funktioniert überall, wo HiveServer2 läuft – Open-Source Apache Hive, CDP Base (Kerberos/LDAP,RTP) and CDP Public / CDWermen (Knox, LDAP over TLS). Auth, Transport and TLS werden vollständig über HIVE_*-UMgebungsvariablen gesteuert.

  • Standardmäßig sicher – execute_query erzwingt eine Schreibschutz-Präfix-Prüfung (S/S/DESCRIBE/WITH); Schreib- and DDL-Anweisungen werden abgelehnt, htt before a Verbindung geöffnet wird.

  • Transport – stdio (Standard), http oder sse, ausgewählt über MCP_TRANSPORT.

  • Tech-Stack – Python ≥3.10, FastMCP, impyla, uv. 11 Unit-Tsts (Verbindung gemockt, kein echtes Hive erforderlich).

Related MCP server: impala-mcp-server

Tokens

Tool

Signatur

description

execute_query

execute_query(query: str) -> str

Führt eine schreibgeschützte SQL-Abfrage auskting2,

SELECT, SHOW, DESCRIBE, WITH) und gibt(sequence. JSON-Array von Objekten mit Spaltennamen

zurück. Schreiboperationen werden mit einer Fehlermeldung abgelehnt.

get_schema

get_schema() -> str

Führt den SHOW TABLES für die konfigurierte Datenbank aus und gibt

die Tabellenliste als JSON-Array von Zeichenketten zurück.

Konfiguration

Alle Konfiguration erfolgt über Umgebungsvariablen (oder eine .env-Datei im Projektstamm).

Variable

Standard

Beschreibung

HIVE_HOST

localhost

Hostname oder IP-Adresse des SQL HiveServer2

HIVE_PORT

10000

Thrift-Port des HiveServer2

HIVE_DATABASE

default

Datenbank, mit der verbunden werden soll

HIVE_USE_SSL

false

TLS for die Verbindung aktiviren

HIVE_KERBEROSERSEVICE_NAME

hive

Kerberos-Service-Rrinzip-Namenu (nur GSSPI)

MCP_TRANSPORT

stdio

MCP-Transport: stdio, http oder sse

Kopieren Sie .env.example nach .env and tragen Sie Ihre Werte ein.

Deployment-Presets

Lokal / Entwicklung (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 mit KerberSteid

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

Beschen Sie sich vor dem Start des Servers ein Kerberos-Ticket (kinit).

Start4

# 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-Konfiguration

MCP-Hosts (likeBlilde Desktop, Clouderatest AI Agent Studio, usw.) regestrieren Server in einem mcpServers- JSON-Block. uvx startet den Server in Deploy automatically from GitHub, no need to local instalation or PyPI-Mublish. Fülle Sie die HIVE_*-Werte for Ihre Umgebung aus (siehe Konfiguration and die obigen Deployment-Presets).

{
    "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"
            }
        }
    }
}

Das obige Beispiel zilt auf CDP Public Cloud / CDW. Für lokale oder Kerberos-Ziele, tauschen Sie die env- Werte mithilfe der obigen Presets aus. @v0.1.0 fixiert eine feste Release-Version – Einfernen Sie (sie) um dem neusten Stand zu folgen, orhöhen Sie sie für eine neuere Version.

Speziell für Cloudera 23 AI Agent Studio (Registrierungsschritte, Behandlung von UmgebungsVvariablen, stdio/uvz-Einschränkungen) siehe examples/agent-studio/.

##,Smoke-Test

  1. Starten Sie eine echtes HiveServer2 (z. B. das Apache-Hive-Docker-Image):

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

  2. Three .env mit the obigen lokalen Preset konfigurieren.

  3. Starten Sie den Server:

    uv run hive-mcp-server

  4. In einerm zweiten Termziel-ain, verwenden Sie den fastmcp-Dev-Inspektor oder einen beliebigen MCP-Client, um beide Tools aufzurufen:

    • get_schema() – sollte [] oder eine Liste der Tabellenamen zurückgeben.

    • execute_query("SHOW DATABASES") – sollte ein JSON-Array der Datenbanken zurückgeben.

Lizenz

Apache License 2.0 – siehe LICENSE.

Available Tools

2 tools
execute_queryA

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

NameRequiredDescriptionDefault
queryYes

Output 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.

NameRequiredDescriptionDefault

No parameters

Output 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.

  1. 2 tool updatesv0.1.0
    • First observedexecute_query
    • First observedget_schema
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.9/5.0

Scored across 2 tools

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

ActivityStale
ResponsivenessNo issues

Resources

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