AI for Database API
60
AI ReadinessFoundational (C+)60/100
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.
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.
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%2Faifordatabase.com%2Faifordatabase" | 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%2Faifordatabase.com%2Faifordatabase" | 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 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
Route sensitive queries through an approval rule before they run
Register webhook endpoints and inspect their delivery history
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
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.
A
AI for Database API
- Foundational (C+)60/100
47
Foundational Compliance62
Developer Experience & Jentic Compatibility49
AI-Readiness & Agent Experience89
Agent Usability60
Security78
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.2
Show dimension breakdown
47
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.
0%
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.
62
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: B-Signals: 4
0%
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.
49
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.
0%
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.
89
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.
60
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).
78
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: A-Signals: 1
78%
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 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.
Related APIs
Alternatives and complements available in the Jentic catalogue.
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.
GET STARTED