Microsoft Azure SearchIndexClient
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For Agents
Query Azure Cognitive Search indexes for full-text and faceted results, run autocomplete and suggester calls, and push document upload, merge, and delete batches.
Use for: Search for documents matching a query in an index, Get a document by its key from a search index, I want to upload a batch of documents to a search index, Retrieve autocomplete suggestions for a partial query
Not supported: Does not create indexes, manage indexers, or provision the search service — use for querying and updating documents inside an existing index only.
Jentic publishes the only available OpenAPI specification for SearchIndexClient, keeping it validated and agent-ready. The Azure Cognitive Search data plane API queries and updates documents inside a search index, supporting full-text search, filtering, faceting, suggesters, autocomplete, and bulk indexing actions. Agents can run queries against an index, page through results, retrieve a single document by key, and push batches of upload, merge, or delete actions. This is the runtime API for any application that reads from or writes to a Cognitive Search index.
Install Jentic One Beta
Connect the SearchIndexClient to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the SearchIndexClient, 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://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | 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 SearchIndexClient API.
Run a full-text or filter-based search across an index with paging and facets
Retrieve a single document by its key from the index
Push a batch of upload, merge, mergeOrUpload, or delete actions to the index
Suggest matching values from a configured suggester for partial input
Autocomplete the next term a user is typing against an index
Count the total documents currently in an index
Use Cases
Patterns agents use SearchIndexClient API for, with concrete tasks.
★ Faceted Search Over a Product Catalogue
E-commerce teams use SearchIndexClient to power product search with category, brand, and price facets. The POST /docs/search.post.search endpoint accepts a query, filter expression, facets list, and select fields, returning ranked documents plus facet counts in a single call. Pagination is handled via $top and $skip parameters so the storefront can render result pages without extra round trips.
POST /docs/search.post.search with body {search: "running shoes", filter: "price lt 150", facets: ["brand,count:10", "category"], top: 20} and return both the value array and @search.facets
Typeahead and Autocomplete Experiences
Search-driven UIs need to surface suggestions as the user types. SearchIndexClient exposes suggester-backed endpoints for both autocomplete (next-word completion) and suggest (matching documents). Each call accepts a partial input and a suggester name configured in the index. Results return inside a few hundred milliseconds for low-latency typeahead.
GET /docs/search.autocomplete?search=lapt&suggesterName=sg&autocompleteMode=oneTermWithContext and return the text values from the response
Bulk Index Maintenance via Batch Actions
When data changes upstream, the indexer pushes batches of upload, merge, mergeOrUpload, and delete actions to keep the index in sync. POST /docs/search.index accepts up to 1,000 documents per call as long as the total payload stays under 16 MB. Per-document failures are reported individually so the caller can retry only the failed entries.
POST /docs/search.index with a value array containing 100 mergeOrUpload actions and inspect response.value for any document where status is false
Agent-Driven Retrieval-Augmented Generation via Jentic
Through Jentic, a RAG agent can search for query azure cognitive search, load the search.post.search input schema, populate it from the user's natural language question, and execute the call with a managed search admin or query key. The retrieved documents feed straight into the agent's prompt context. The same flow handles autocomplete and suggester calls for chat-style search experiences.
Search Jentic for query azure cognitive search, load the search.post.search schema, execute it with a query and select=title,content, and return the top 5 documents to the LLM context window
Key Endpoints
9 endpoints — jentic publishes the only available openapi specification for searchindexclient, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
POST
/docs/search.post.search
Run a full search query
GET
/docs
Run a search via query parameters
GET
/docs('{key}')
Get a document by key
POST
/docs/search.index
Batch upload, merge, or delete documents
GET
/docs/search.autocomplete
Autocomplete a partial query
POST
/docs/search.post.suggest
Suggest documents via a suggester
GET
/docs/$count
Count documents in the index
POST
/docs/search.post.search
Run a full search query
GET
/docs
Run a search via query parameters
GET
/docs('{key}')
Get a document by key
POST
/docs/search.index
Batch upload, merge, or delete documents
GET
/docs/search.autocomplete
Autocomplete a partial query
POST
/docs/search.post.suggest
Suggest documents via a suggester
GET
/docs/$count
Count documents in the index
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
Azure Cognitive Search admin and query keys are stored encrypted in the Jentic vault. Agents receive scoped api-key headers — the raw key never enters the agent context, and Jentic can rotate the underlying key without changing agent code.
Intent-based discovery
Agents search by intent such as query azure cognitive search or autocomplete search index, and Jentic returns the matching SearchIndexClient operation with its full input schema for query, filter, facet, and batch action shapes.
Time to first call
Direct integration: 1-2 days to wire api-key handling, query DSL, and batch error retries. Through Jentic: under an hour to search, load, and execute a search call.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using SearchIndexClient API through Jentic.
Why is there no official OpenAPI spec for SearchIndexClient?
Microsoft Azure does not publish a unified OpenAPI specification for the Cognitive Search data plane. Jentic generates and maintains this spec so that AI agents and developers can call SearchIndexClient via structured tooling. It is validated against the live search.windows.net data plane and kept up to date. Get started at https://app.jentic.com/sign-up.
What authentication does SearchIndexClient use?
It uses an API key in the api-key request header. Each search service has separate admin keys (for indexing) and query keys (for read-only search). Jentic stores these keys in its vault so the agent never holds the raw value.
Can I run faceted search with this API?
Yes. POST /docs/search.post.search with a body that includes facets, filter, and search. The response returns matched documents plus the @search.facets object containing per-facet bucket counts. You can request multiple facets and limit the count per facet.
How many documents can I push in a single batch?
POST /docs/search.index accepts up to 1,000 actions per call as long as the total payload stays under 16 MB. The response array reports per-document status so you can retry only the entries that failed without re-sending the whole batch.
What are the rate limits for SearchIndexClient?
Throughput depends on the search service tier you provisioned. Free and Basic tiers have lower QPS ceilings than Standard tiers, and replicas multiply read throughput. Throttled requests return HTTP 503 with a Retry-After header.
How do I run a search query through Jentic?
Search Jentic for query azure cognitive search, call client.load on the search.post.search operation to receive its input schema, then call client.execute with the search query, filter, and top values. Jentic injects the api-key header and returns the response value array.
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