Install Jentic One Beta

Connect the Dataproc Metastore API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Dataproc Metastore API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fgoogleapis.com%2Fgoogle-dataproc-metastore" | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fgoogleapis.com%2Fgoogle-dataproc-metastore" | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Dataproc Metastore API.

Provision and delete managed Hive metastore services in a Google Cloud project location

Create and list backups of metastore services for disaster recovery

Import existing Hive metadata from Cloud Storage into a metastore service

Create and manage federations that combine multiple metastores under one endpoint

GET STARTED

Start building with Dataproc Metastore API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Dataproc Metastore API for, with concrete tasks.

★ Managed Hive metastore for Dataproc

Provision a Dataproc Metastore service so that ephemeral Dataproc clusters share a persistent Hive metadata catalog rather than each cluster maintaining its own. This lets jobs run against the same tables and partitions across cluster lifetimes and integrates cleanly with BigQuery federation queries.

Create a metastore service named lake-meta under projects/acme/locations/europe-west1 with hive metastore version 3.1.2 and tier DEVELOPER, then poll the returned operation

Metastore backup and restore

Run scheduled backups of a metastore service so that table definitions, partitions, and database metadata can be restored after an accidental drop or schema corruption. The backups resource is per-service and pairs with the import flow for restore-style workflows.

Create a backup of metastore service projects/acme/locations/europe-west1/services/lake-meta and confirm the operation completes

Metastore federation across regions

Combine multiple Dataproc Metastore services under a single federation endpoint so that data engines see one logical metastore for queries spanning regions or business units. Federations are useful for organisations that keep separate metastores per data domain but want unified access.

Create a federation named global-lake under projects/acme/locations/global with backing services from europe-west1 and us-central1

Agent integration via Jentic

AI agents managing data lake metadata can use the Dataproc Metastore API through Jentic to provision services, run scheduled backups, or import metadata from Cloud Storage URIs without holding the underlying OAuth refresh token in their prompt.

List metadataImports for service lake-meta and start a new import from gs://acme-lake/metadata/2026-06.dump if no import has run in the last 7 days

Key Endpoints

27 endpoints — dataproc metastore is google cloud's managed apache hive metastore service that provides a centralised metadata catalog for dataproc, bigquery federation, and other open-source data engines.

METHOD

PATH

DESCRIPTION

GET

/v1/{+parent}/services

List metastore services

DELETE

/v1/{+name}

Delete a metastore service or other resource

GET

/v1/{+parent}/backups

List backups for a service

GET

/v1/{+parent}/federations

List federations in a location

GET

/v1/{+parent}/metadataImports

List metadata import runs

GET

/v1/{+parent}/migrationExecutions

List migration executions

GET

/v1/{+resource}:getIamPolicy

Read IAM policy on a service

POST

/v1/{+name}:cancel

Cancel a long-running operation

GET

/v1/{+parent}/services

List metastore services

DELETE

/v1/{+name}

Delete a metastore service or other resource

GET

/v1/{+parent}/backups

List backups for a service

GET

/v1/{+parent}/federations

List federations in a location

GET

/v1/{+parent}/metadataImports

List metadata import runs

GET

/v1/{+parent}/migrationExecutions

List migration executions

GET

/v1/{+resource}:getIamPolicy

Read IAM policy on a service

POST

/v1/{+name}:cancel

Cancel a long-running operation

Why Jentic?

What agents get from Jentic-routed access to this vendor.

Setup

Wiring the Dataproc Metastore API by hand means setting up Google OAuth 2.0 with the cloud-platform scope, refreshing bearer tokens, and polling long-running service operations yourself against the metastore.googleapis.com host. Through Jentic you install once, import the Dataproc Metastore API from the API Directory, store the OAuth credential once, and your agent calls it.

Permission scoping

The Metastore API puts the service and resource name in the URL path (/v1/{+parent}/... and /v1/{+name}), so a rule can pin your agent to one metastore service: it can list backups, federations, and metadata imports there and nothing else. You choose the operations it may call, so destructive ones like service deletion are not included unless you add them.

Credential isolation

Your Google OAuth credential is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'provision a metastore service' or 'import Hive metadata', and Jentic returns the matching Dataproc Metastore operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using Dataproc Metastore API through Jentic.

What authentication does the Dataproc Metastore API use?

The API uses Google OAuth 2.0 with the https://www.googleapis.com/auth/cloud-platform scope. Through Jentic, the OAuth credentials are held in the encrypted vault and the agent receives only a scoped bearer token; the refresh token never enters the agent's context.

Can I federate multiple metastore services through this API?

Yes, the federations endpoints under a project location let you combine multiple metastore services under a single federation endpoint, useful when teams want one logical catalog backed by per-region metastores.

What are the rate limits for the Dataproc Metastore API?

Quotas are managed per project under the Dataproc Metastore service in the Cloud Console quotas page. Long-running operations have concurrency caps in addition to per-minute request limits, so bulk lifecycle scripts should respect the operation queue.

How do I import existing Hive metadata through Jentic?

Search Jentic for import hive metadata to dataproc metastore, load the metadataImports create operation, and execute with the gs:// URI of the metadata dump and the target service name. Jentic returns the long-running operation handle for polling.

Is the Dataproc Metastore API free?

The API itself is not separately billed; you pay for the metastore service tier (DEVELOPER or ENTERPRISE) per hour and any associated storage. See the Dataproc Metastore pricing page for current rates.

Can I run Hive queries through this API?

No, this API manages metastore services themselves. To run queries, point a Dataproc cluster, BigQuery federation, or Spark job at the metastore endpoint and execute Hive or Spark SQL there.

Can I limit what my agent is allowed to do with the Dataproc Metastore API?

Yes. Because your Jentic One instance is self-hosted, you write the rules that decide which operations and credentials your agent may use. Since this API puts the service and resource name in the URL path, you can pin the agent to a single metastore service so it only lists backups, federations, and metadata imports there and touches nothing else. You also choose the exact operations it may call, so destructive ones like service deletion stay out of reach unless you explicitly add them.

Google Dataproc Metastore API

Browse all Google APIs

AI ReadinessFoundational (D-)41/100

See full scorecard

Official vendor OpenAPI document · agent-readyStorageDatabaseoauth227 EndpointsREST

For Agents

Provision and manage Dataproc Metastore services that act as a managed Hive metastore for Dataproc and BigQuery: create services, run backups, import metadata, and federate metastores.

Use for: Provision a new Dataproc Metastore service in europe-west1, Trigger a backup of an existing Dataproc Metastore service, Import Hive metadata from a Cloud Storage URI into my metastore, Create a federation that combines two metastores under one endpoint

Not supported: Does not handle Hive query execution, table data files, or cluster compute - use for metastore service lifecycle, backups, metadata imports, federations, and IAM policy only.

Dataproc Metastore is Google Cloud's managed Apache Hive metastore service that provides a centralised metadata catalog for Dataproc, BigQuery federation, and other open-source data engines. The API covers metastore service lifecycle, backups, metadata import, federations across multiple metastores, and IAM policy management on individual services. Long-running operations are returned for state-changing calls and most resources are scoped to a project location.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: BSignals: 4

67%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

Specification Validity

Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).

100%

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: CSignals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

25%

Response Coverage

Percentage of operations with complete response definitions (success, client error, server error).

100%

Tooling Readiness

Health of API ingestion, bundling, and resolution within Jentic pipelines.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: FSignals: 4

74%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: A+Signals: 1

94%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: B-Signals: 1

62%

Authentication Strength

Average quality of security schemes based on authentication method strength (weakest link for OAuth2).

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: B-Signals: 1

62%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Dataproc Metastore API to your agent

Capabilities

Start building with Dataproc Metastore API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Google Dataproc Metastore API

Jentic AI Readiness Score

Dataproc Metastore API

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness