Contextual AI API
71
AI ReadinessAI-Aware (A-)71/100
Agent-ready OpenAPI document · curated by JenticAI/MLEmbeddings Searchbearer3 EndpointsREST
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For Agents
List and create RAG agents on the Contextual AI platform and list datastores that those agents query, authenticated with a bearer token.
Use for: List all Contextual AI agents in the workspace, Create a new Contextual AI RAG agent for a finance-policy use case, Find which datastores are available to back a new agent, Get the configuration of a specific Contextual AI agent
Not supported: Does not handle direct LLM inference, embedding generation, or end-user chat - use for managing Contextual AI RAG agents and listing datastores only.
Jentic publishes the only available OpenAPI specification for Contextual AI API, keeping it validated and agent-ready. Contextual AI is an enterprise platform for building production-grade Retrieval-Augmented Generation (RAG) agents that combine retrieval against governed datastores with grounded language-model responses. The current public API surface exposes endpoints to list and create agents and to list datastores, with bearer-token authentication scoped to the customer's workspace.
Install Jentic One Beta
Connect the Contextual AI API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Contextual AI 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%2Fcontextual.ai%2Fcontextual" | 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%2Fcontextual.ai%2Fcontextual" | 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 Contextual AI API.
List the RAG agents configured in the Contextual AI workspace
Create a new RAG agent definition tied to one or more datastores
List the datastores available to be attached to an agent
Authenticate per request with a bearer API key scoped to the workspace
Inspect agent configuration to understand what each production RAG agent is wired to
Use Cases
Patterns agents use Contextual AI API for, with concrete tasks.
★ Provisioning Production RAG Agents
Platform engineering teams use POST /agents to provision new Contextual AI agents from infrastructure-as-code or admin tooling, instead of clicking through the dashboard. Each agent is bound to a datastore, so agents are repeatable and code-reviewed. This standardises how RAG agents are launched across multiple business lines.
POST /agents with name='policy-bot' and a datastore reference for the finance-policy datastore, then verify the agent is listed via GET /agents
Datastore Inventory and Governance
Compliance and data-governance teams use GET /datastores to inventory the datastores backing the workspace's RAG agents and confirm that each agent is connected to a sanctioned source. The list can be cross-referenced against the agents returned by GET /agents to catch agents pointing to deprecated datastores. This avoids RAG agents quietly serving answers from outdated content.
GET /datastores, GET /agents, and produce a report of agents whose configured datastore is no longer in the active datastore list
AI Agent Bootstrap via Jentic
An AI agent helping an internal platform engineer uses Jentic to create new Contextual AI agents on demand. The agent reads the schema, fills in name and datastore, and POSTs to /agents, then returns the new agent id. Through Jentic the bearer token is held in your Jentic One instance so the agent never sees the raw key.
Search Jentic for 'create a contextual ai agent', execute with name='hr-bot' and the datastore_id for the HR-policies datastore, and return the new agent id
Key Endpoints
3 endpoints — jentic publishes the only available openapi specification for contextual ai api, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
GET
/agents
List RAG agents in the workspace
POST
/agents
Create a new RAG agent
GET
/datastores
List datastores available in the workspace
GET
/agents
List RAG agents in the workspace
POST
/agents
Create a new RAG agent
GET
/datastores
List datastores available in the workspace
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.
C
Contextual AI API
- AI-Aware (A-)71/100
93
Foundational Compliance63
Developer Experience & Jentic Compatibility56
AI-Readiness & Agent Experience94
Agent Usability60
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
93
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: A+Signals: 4
99%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
100%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
75%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
63
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.
50%
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.
56
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: CSignals: 4
64%
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.
60%
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.
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).
100
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: A+Signals: 1
100%
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 Contextual AI API by hand means sending a bearer token on every request and mapping the agent and datastore operations yourself. Through Jentic you install once, import the Contextual AI API from the API Directory, store the token once, and your agent calls it.
Permission scoping
The Contextual AI API takes its targets in the request body rather than the URL path, so you limit the agent to the operations it needs, such as listing agents or listing datastores, and it can call nothing outside that set. Creating an agent is only reachable if you include that operation in the allowed set.
Credential isolation
Your Contextual AI 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 'create a RAG agent' or 'list datastores', and Jentic returns the matching Contextual AI 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 Contextual AI API through Jentic.
Why is there no official OpenAPI spec for Contextual AI API?
Contextual AI publishes API reference docs at docs.contextual.ai but does not host a stable OpenAPI 3 file at a permanent URL. Jentic generates and maintains this spec so that AI agents and developers can call Contextual AI API via structured tooling. It is validated against the live API and kept up to date. Get started with Jentic One, the self-hosted execution layer.
What authentication does the Contextual AI API use?
Each request must include an Authorization: Bearer header with a workspace API key. Through Jentic the bearer token is stored in your Jentic One instance and applied at call time, so the agent does not handle the raw key.
Can I create a new RAG agent with the Contextual AI API?
Yes. POST /agents creates a new agent record bound to a datastore, and the new agent appears in GET /agents. The agent can then be invoked through Contextual AI's runtime endpoints once provisioning completes.
What are the rate limits for the Contextual AI API?
Contextual AI applies per-workspace rate limits sized to the customer's plan; current limits and usage are visible in the Contextual AI dashboard. Through Jentic, retries with exponential backoff are handled at the SDK level so transient throttling does not surface to agent code.
How do I list datastores with the Contextual AI API through Jentic?
Run `jentic.search('list contextual ai datastores')`, load the operation, and execute. Jentic resolves this to GET /datastores and returns the datastores available in the workspace ready to attach to a new agent.
Does the Contextual AI API expose runtime query endpoints?
The current OpenAPI surface covers agent and datastore management via /agents and /datastores; the runtime query endpoints for invoking an agent are documented separately in Contextual AI's API reference. Use this spec for provisioning and inventory, and contact Contextual AI for the runtime invocation endpoint shape.
Can I limit what my agent is allowed to do with the Contextual AI API?
Yes. Because you run Jentic One yourself, your own rules decide which Contextual AI operations and credentials the agent may use, so you can allow it only to list agents (GET /agents) and list datastores (GET /datastores) and nothing else. Since the API takes its targets in the request body rather than the URL path, creating an agent (POST /agents) is reachable only if you include that operation in the allowed set. The operator controls this scope, and the bearer token stays in your instance rather than the agent's context.
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