Install Jentic One Beta

Connect the AI for Database API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the AI for Database 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%2Faifordatabase.com%2Faifordatabase" | 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%2Faifordatabase.com%2Faifordatabase" | 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 AI for Database API.

Introspect a database connection's schema before composing a query

Translate a natural-language chat message into an executable query

Run saved queries on demand and read back their results

Assemble dashboards with widgets backed by live query results

Define named metrics and read their current computed value

GET STARTED

Start building with AI for Database API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use AI for Database API for, with concrete tasks.

★ Natural-language data answers inside an agent

An AI agent connected through Jentic answers business questions directly from live database data: it reads the connection schema, composes a query from the user's natural-language request, executes it, and returns the rows. Because the agent discovers operations by intent, it can go from a question like 'what were our top five accounts last quarter' to a real result without a developer hand-writing the query path.

Introspect the schema for a connection, build a query that returns the top five accounts by revenue for the last quarter, execute it, and return the rows

Governed query execution with approvals

Teams that let an agent touch production data can gate risky statements behind approval rules. A query the agent submits is held pending until a human approves or rejects it, and the agent can poll the decision before anything runs. This keeps destructive or large statements under review while routine reads flow through, so data access can be delegated without handing over unconditional write power.

Submit a bulk update query for approval, poll its status until a reviewer approves it, then confirm the statement executed

Automated dashboards and metrics

The API lets an agent assemble reporting on top of a connection: it creates dashboards, adds widgets bound to query results, and defines named metrics whose values it can read back on demand. This turns ad hoc questions into durable, reusable reporting that an agent can refresh and cite later, rather than recomputing the same figures from scratch each time.

Create a dashboard, add a widget bound to a monthly-revenue query, define a metric for active users, and read back its current value

Key Endpoints

81 endpoints — the ai for database api connects an agent to your sql and nosql databases and turns natural-language chat into real queries against them.

METHOD

PATH

DESCRIPTION

POST

/connections

Create a database connection

POST

/connections/{id}/query

Execute a query on a connection

POST

/chat

Send a natural-language message

GET

/dashboards

List dashboards

POST

/dashboards/{id}/widgets

Create a dashboard widget

POST

/saved-queries/{id}/run

Execute a saved query

GET

/metrics/{id}/value

Get a metric's current value

POST

/queries/submit

Submit a query for approval

POST

/connections

Create a database connection

POST

/connections/{id}/query

Execute a query on a connection

POST

/chat

Send a natural-language message

GET

/dashboards

List dashboards

POST

/dashboards/{id}/widgets

Create a dashboard widget

POST

/saved-queries/{id}/run

Execute a saved query

GET

/metrics/{id}/value

Get a metric's current value

POST

/queries/submit

Submit a query for approval

Why Jentic?

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

Setup

Wiring the AI for Database API by hand means handling its bearer-token auth, learning its connection and query model, and managing the approval flow for writes yourself. Through Jentic you install once, import the API from the Directory, store the token once, and your agent calls it.

Permission scoping

The API puts the connection id in the URL path, so a rule can pin your agent to one connection and the operations you pick: you can allow schema reads and query execution while leaving connection deletion or credential rotation out unless you add them.

Credential isolation

Your bearer token 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 'run a query on a database' or 'build a dashboard widget', and Jentic returns the matching AI for Database operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using AI for Database API through Jentic.

What authentication does the AI for Database API use?

The AI for Database API authenticates with a bearer token sent in the Authorization header, as declared in its OpenAPI spec. Through Jentic the token is stored once by your own self-hosted instance and injected at call time, so it never appears in the agent's prompt or logs.

Can I run queries against my own database with the AI for Database API?

Yes. You register a connection, introspect its schema, and run read or write queries against it, and the API can translate a natural-language chat message into the query it executes. You can also save a query and rerun it later or submit it for approval first.

Is there an AI for Database API MCP server?

You do not need an MCP server to give your agent the AI for Database API. Jentic connects it directly from the API Directory: import the API, store your bearer token once, and your agent calls its operations on demand without loading another server's tool definitions into its context.

Can I limit what my agent is allowed to do with the AI for Database API?

Yes. Write a rule that allows only the schema-read and query-execution operations for one connection, so the agent can answer questions from that database and nothing else, and route any write through the approval queue. Every call it makes is logged for review.

What are the rate limits for the AI for Database API?

The OpenAPI spec does not specify rate limits for the AI for Database API. Check the vendor documentation at https://www.aifordatabase.com for current limits before running high-volume query workloads.

How do I answer a data question with the AI for Database API through Jentic?

Search Jentic for an intent such as 'query a connected database', and it returns the matching operation with its input schema so your agent can introspect the schema, run the query, and read back the rows. To run it on your own infrastructure, install Jentic One from its GitHub repo.

AI for Database API

AI ReadinessFoundational (C+)60/100

See full scorecard

Official vendor OpenAPI document · agent-readyAnalyticsBusiness Intelligencebearer81 EndpointsREST

For Agents

Query connected SQL and NoSQL databases from natural language, run and save queries, define metrics, and build dashboards and widgets, with an approval step for sensitive statements.

Use for: I need to run a SQL query against a connected database, Translate this question about our sales data into a query and run it, List all the database connections available to me, Create a dashboard widget that charts last month's revenue

Not supported: Does not handle database hosting, provisioning, or backups. Use for querying connected databases and building reporting on top of them only.

The AI for Database API connects an agent to your SQL and NoSQL databases and turns natural-language chat into real queries against them. It introspects a connection's schema, runs read and write queries, saves and reruns named queries, and routes risky statements through an approval workflow before they execute. Beyond querying, it builds dashboards and widgets, defines reusable metrics, and fires webhooks on activity, so an agent can both answer questions from live data and assemble reporting on top of it.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: D+Signals: 4

73%

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).

17%

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: B-Signals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

47%

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: D+Signals: 4

32%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

100%

OperationId Quality

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

63%

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: ASignals: 1

89%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: B-Signals: 1

60%

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: A-Signals: 1

78%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the AI for Database API to your agent

Capabilities

Start building with AI for Database API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

AI for Database API

Jentic AI Readiness Score

AI for Database 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