Context Link API

Name: Context Link API API
Brand: Context Link API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLEmbeddings SearchapiKey1 EndpointsREST

For Agents

Retrieve grounded answers from a customer's connected knowledge sources by passing a search query to a single context endpoint authenticated via an X-API-Key header.

Quickstart

Get started with Context Link API in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"retrieve grounded context with context link"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with Context Link API API.

Send a natural-language query and receive context passages from the connected knowledge base

Use a single GET /api/v1/context endpoint to power retrieval-augmented generation flows

Authenticate per request with an X-API-Key header so each agent or app uses its own scoped key

Pass query strings that include user intent rather than crafting vector embeddings manually

GET STARTED

Start building with Context Link API API

Explore with Jentic

View OpenAPI Document

Use for: Search the connected knowledge base for documentation about a feature, Retrieve grounding context for an LLM answer about an internal process, Find the most relevant passage in the knowledge base for a user question, Get cited context to back up a chatbot response

Not supported: Does not handle source ingestion, embedding configuration, or LLM generation — use for retrieving grounded context passages from connected knowledge sources only.

Jentic publishes the only available OpenAPI document for Context Link API, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Context Link API, keeping it validated and agent-ready. Context Link is a context-aware retrieval API that takes a natural-language query and returns answers grounded in the customer's connected knowledge sources, suitable for RAG-style assistants and chat interfaces. Authentication uses an X-API-Key header and the single GET /api/v1/context endpoint accepts a query string and returns the matched context passages.

Use Cases

Patterns agents use Context Link API API for, with concrete tasks.

★ RAG-Powered Customer Support Chatbot

Support teams plug Context Link into a customer-facing chatbot to ground responses in their knowledge base, product docs, or help centre. The chatbot sends each user question to GET /api/v1/context, then passes the returned passages to an LLM to compose an answer with citations. This avoids the chatbot hallucinating product behaviour and keeps responses tied to vetted source content.

GET /api/v1/context?query='How do I reset my account password?' with the X-API-Key header, then pass the returned passages to an LLM to draft a cited answer

Internal Knowledge Assistant

Internal IT or HR teams expose a single retrieval endpoint to their employees so policy, process, and onboarding questions resolve against a curated knowledge source. Context Link returns the matched passages so the assistant can answer in plain language and link to the source. Setup is faster than building a vector index because Context Link manages embeddings on the customer's side.

Send a user query like 'what is our remote-work policy for engineers' to GET /api/v1/context and return the highest-scoring passage with its source link

AI Agent Retrieval Step via Jentic

AI agents that compose multi-step workflows use Context Link as their retrieval step before calling an LLM for synthesis. Through Jentic the agent searches for 'retrieve grounded context', loads the schema, and executes the call with the user query. The X-API-Key is held in MAXsystem so the agent never sees the raw key.

Search Jentic for 'retrieve grounded context with context link', execute with query='latest API change log', and pass the returned passages to a downstream summarisation step

Key Endpoints

1 endpoints — jentic publishes the only available openapi specification for context link api, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

GET

/api/v1/context

Retrieve grounded context passages for a search query

GET

/api/v1/context

Retrieve grounded context passages for a search query

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Context Link X-API-Keys are stored encrypted in the Jentic vault (MAXsystem). Agents receive scoped access at call time and never see the raw API key, so multi-tenant agents can serve different Context Link customers without leaking keys between sessions.

Intent-based discovery

Agents search Jentic by intent (e.g. 'retrieve grounded context') and Jentic returns the single Context Link operation with its query parameter schema, so the agent calls the right endpoint without inspecting Context Link docs.

Time to first call

Direct Context Link integration: half a day to wire up the X-API-Key, build the request, and parse passages. Through Jentic: under 15 minutes to search, load schema, and execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Pinecone API

Pinecone is a managed vector database where the developer controls embeddings and indexes directly.

Choose Pinecone when fine control over embedding model, namespaces, and metadata filtering is needed; choose Context Link for a managed end-to-end retrieval endpoint.

Alternative

Exa Search API

Exa retrieves grounded passages from the open web rather than a customer's connected knowledge sources.

Choose Exa for web-scale retrieval; choose Context Link when answers must come from internal customer-owned content.

Alternative

Tavily Search API

Tavily provides agent-friendly web search with citations as a single endpoint.

Choose Tavily for general web research steps in an agent; choose Context Link to retrieve from customer-private knowledge sources.

Complementary

OpenAI API

OpenAI generates the final answer from passages returned by Context Link.

Use Context Link to fetch grounded passages and OpenAI to synthesise a cited answer for the end user.

FAQs

Specific to using Context Link API API through Jentic.

Why is there no official OpenAPI spec for Context Link API?

Context Link documents the context endpoint at context-link.ai/docs but does not publish a stable OpenAPI 3 file. Jentic generates and maintains this spec so that AI agents and developers can call Context Link API via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Context Link API use?

Each request must include an X-API-Key header issued from the Context Link dashboard. Through Jentic the API key is stored in the MAXsystem vault and applied at call time, so the agent does not see the raw value.

Can I retrieve grounded answers with the Context Link API?

Yes. GET /api/v1/context with a query parameter returns the most relevant passages from the connected knowledge sources, which an LLM can then cite when composing an answer.

What are the rate limits for the Context Link API?

Context Link applies per-API-key rate limits sized to the customer's plan; quotas and current usage are visible in the Context Link 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 run a retrieval through the Context Link API via Jentic?

Run `jentic.search('retrieve grounded context')`, load the operation, and execute with the user's question as the query. Jentic resolves this to GET /api/v1/context and returns the passages ready to feed into an LLM.

Does the Context Link API replace a vector database?

Functionally it covers the retrieval step that a vector database plus embedding pipeline would provide, but it is a managed API rather than a database; the customer connects sources and Context Link runs the embedding and ranking. Pair it with Pinecone or Weaviate only if more direct vector control is required.