Google Cloud Data Catalog API
Browse all Google APIs40
AI ReadinessFoundational (D-)40/100
Official vendor OpenAPI document · agent-readyAnalyticsData Pipelinesoauth235 EndpointsREST
For Agents
Discover BigQuery, Pub/Sub, and other data assets, attach business tags, and search the catalog so an agent can locate and describe the right dataset for a query.
Use for: I need to find a BigQuery table by its business name, Search the catalog for tables tagged 'pii', Retrieve the schema and tags of a specific entry, Look up the entry for a given Cloud Storage bucket
Not supported: Does not query data, run pipelines, or scan content for sensitive values - use only to manage and search metadata about data assets.
Google Cloud Data Catalog API is a fully managed metadata service for discovering, describing, and governing data assets across BigQuery, Pub/Sub, Cloud Storage, and external sources. It exposes catalog search, entry groups, entries, tag templates, and tags so that data stewards can attach business context to columns and tables. Search supports keyword, fully qualified name, and metadata filter syntax. The API also handles IAM at every level so that data discovery can be opened to analysts while edits remain restricted to stewards.
Install Jentic One Beta
Connect the Google Cloud Data Catalog API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Google Cloud Data Catalog 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.
1
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%2Fdatacatalog" | sh2
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%2Fdatacatalog" | sh
jentic register # connects your agent to your Jentic One instanceJentic 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 Google Cloud Data Catalog API.
Search the catalog by keyword, fully qualified name, system, or tag value
Look up an entry by its linked resource (e.g. a BigQuery table URI)
Define and manage tag templates with typed fields for governed metadata
Attach tag instances to entries and column-level paths
Manage entry groups for custom data sources outside Google's first-party systems
Set IAM policies on entry groups, entries, and tag templates
Star and rename entries to support analyst workflows
Use Cases
Patterns agents use Google Cloud Data Catalog API for, with concrete tasks.
★ Self-Service Data Discovery
Analysts use a portal that calls catalog.search with their plain-language query and a system filter on bigquery. Data Catalog returns ranked entries with descriptions, owners, and tags. Analysts find the right table in seconds rather than asking on Slack.
Run catalog.search with query='customer churn' and scope.includeProjectIds=['analytics-prod'], return the top 10 entries with their linkedResource and description.
PII Tagging Programme
A data governance team defines a 'pii_classification' tag template and runs tagging workflows that attach the tag to every column flagged by a scanner. Data Catalog stores tags at the column path so SQL editors and BI tools can warn before query authors export PII fields.
Create a tag from template 'pii_classification' on entry 'projects/p/locations/l/entryGroups/g/entries/e' with field column='email' and value='HIGH'.
Custom Source Cataloguing
A platform team registers entries for tables stored in Snowflake or an on-prem Hive metastore so analysts can find them through the same Data Catalog search experience as native BigQuery tables. Custom entry groups and entries hold the schema, owner, and tags. Search treats native and custom entries uniformly.
Create an entryGroup 'snowflake' in us-central1 and add an entry of type TABLE with linkedResource pointing to 'snowflake://acme/orders'.
AI Agent Data Lookup
An analytics AI agent receives 'how many customers signed up last month' and asks Jentic for the right BigQuery table by searching Data Catalog with the question. The agent then issues a BigQuery SQL query against the matched entry. Jentic isolates the Google service account credential and rotates tokens per call.
Run catalog.search with query='customer signups' and system='BIGQUERY', return the top match's linkedResource and description for downstream SQL generation.
Key Endpoints
35 endpoints — google cloud data catalog api is a fully managed metadata service for discovering, describing, and governing data assets across bigquery, pub/sub, cloud storage, and external sources.
METHOD
PATH
DESCRIPTION
POST
/v1/catalog:search
Search the catalog by query and filters
GET
/v1/entries:lookup
Look up an entry by linkedResource or fully qualified name
GET
/v1/{+name}
Get a specific entry, entry group, or tag template
POST
/v1/{+name}/operations
List long-running operations on a resource
POST
/v1/catalog:search
Search the catalog by query and filters
GET
/v1/entries:lookup
Look up an entry by linkedResource or fully qualified name
GET
/v1/{+name}
Get a specific entry, entry group, or tag template
POST
/v1/{+name}/operations
List long-running operations on a resource
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
G
Google Cloud Data Catalog API
- Foundational (D-)40/100
43
Foundational Compliance56
Developer Experience & Jentic Compatibility19
AI-Readiness & Agent Experience94
Agent Usability63
Security64
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
43
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: D-Signals: 4
58%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
0%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
14%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
56
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: CSignals: 4
0%
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.
19
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: FSignals: 4
77%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
0%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
0%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
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.
63
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).
64
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: BSignals: 1
64%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the Data Catalog API by hand means setting up Google OAuth, minting short-lived cloud-platform tokens from a service account rather than a raw JSON key, and addressing catalog and entry resources on datacatalog.googleapis.com. Through Jentic you install once, import the Data Catalog API from the API Directory, store the Google credential once, and your agent calls it.
Permission scoping
The API carries the entry resource name in the URL path (/v1/{name}), so a rule can pin your agent to a given entry, while catalog search accepts a scope you define in the request. You choose the operations it may call, so it can search the catalog and look up entries while anything you leave out stays unavailable.
Credential isolation
Your Google service account 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 'find a BigQuery table', and Jentic returns the matching Data Catalog search or lookup operation with its input schema, including scope and filter syntax, so the agent calls the right endpoint without browsing the reference docs.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Google Cloud Data Catalog API through Jentic.
What authentication does the Google Cloud Data Catalog API use?
The Data Catalog API uses Google OAuth 2.0 with the cloud-platform scope. Through Jentic the service account credentials are kept encrypted in your Jentic One instance and the agent receives short-lived access tokens minted per request.
Can I search Data Catalog for BigQuery tables by business description?
Yes. Call /v1/catalog:search with a query string referencing the description text and a scope filter on system='BIGQUERY'. Results include entries with their linkedResource (the BigQuery table URI), schema, and tags.
What are the rate limits for the Google Cloud Data Catalog API?
Data Catalog enforces per-project quotas with stricter limits on catalog.search and tag mutations than on entry reads. Inspect the Cloud Console Quotas page for the precise per-method limits in your project.
How do I tag a BigQuery column with PII through Jentic?
Run pip install jentic, search Jentic for 'tag bigquery column data catalog', load the schema for tags.create on datacatalog.googleapis.com, and execute it with the entry name, tag template reference, and column path.
Does Data Catalog query the underlying data?
No. It only manages metadata: schemas, descriptions, tags, and lineage hints. Querying a discovered BigQuery table or Cloud Storage object happens through the respective service API.
Can I limit what my agent is allowed to do with the Google Cloud Data Catalog API?
Yes. Because you run Jentic One yourself, your own rules decide which Data Catalog operations and credentials the agent may use, so you can allow read paths like catalog:search and entries:lookup while withholding tag or IAM mutations. Since the entry resource name travels in the URL path (/v1/{name}), a rule can pin the agent to a specific entry or entry group, and the search scope you set in the request bounds what it can look across. Any operation you do not grant stays unavailable to the agent.
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