drupal-search-api

name	drupal-search-api
description	Manage Drupal Search API servers, indexes, fields, processors, and indexing operations. Use when creating search infrastructure, configuring search backends (Database, Solr, AI/vector search), adding fields or processors to indexes, or running indexing operations.
metadata	{"category":"drupal","drupal_version":"11.x","skill_version":"1.0.0","last_updated":"2026-03-21","source_module":"ai_agents_search_api","source_project":"https://www.drupal.org/project/ai_agents","requires":"search_api","tool_count":13}

Drupal Search API

Manage search servers, indexes, fields, processors, and indexing operations for a Drupal site using the Search API module. This skill covers the full lifecycle of search configuration -- from enabling a backend and creating a server, through index and field setup, to processor tuning and indexing operations.

Converted from the ai_agents_search_api Drupal AI agent (part of the AI Agents project).

When to Use

Trigger this skill when:

• User asks to "set up search" or "configure Search API"
• User wants to create or manage a search server or index
• User needs to add, remove, or list fields on a search index
• User asks about search processors (HTML filter, stemmer, tokenizer, etc.)
• User wants to run indexing, reindex, or check indexing status
• User mentions Solr, Elasticsearch, database search, or AI/vector search in Drupal
• User asks about faceted search, full-text search, or semantic search setup

Key Concepts

• Server: A search backend connection (Database, Solr, Elasticsearch, AI Search). Each server can host multiple indexes.
• Index: A searchable collection of content. Each index belongs to one server and defines which content types to index, what fields to expose, and which processors to apply.
• Datasource: Defines what content an index tracks (e.g. entity:node, entity:taxonomy_term, entity:user). An index can have multiple datasources.
• Fields: Properties from content entities that are indexed and made searchable. Each field has a type (string, text, integer, date, boolean, decimal).
• Processors: Plugins that alter how content is indexed and searched (e.g. HTML stripping, tokenization, stemming, content access).
• Tracker: Tracks which items need indexing. The default tracker handles this automatically.

Available Backends

Backend ID	Name	Description
search_api_db	Database	Built-in backend using the site database. Good for small-to-medium sites. No extra infrastructure needed.
search_api_solr	Solr	Apache Solr backend for advanced full-text search. Requires a Solr server.
search_api_ai_search_backend	AI Search	Vector/semantic search backend from the AI module. Uses embeddings for RAG and semantic search capabilities.

Tool Inventory

The following 13 tools are available for search management:

#	Tool ID	Label	Description
1	list_servers	List Search API Servers	Lists all servers with ID, name, backend, status, and availability.
2	get_server	Get Search API Server	Gets detailed info about a server including backend config, status, and connected indexes.
3	create_server	Create Search API Server	Creates a new server with a specified backend. For search_api_db, sensible defaults are applied automatically.
4	delete_server	Delete Search API Server	Deletes a server. The server cannot be deleted if it still has indexes attached -- remove or reassign indexes first.
5	list_indexes	List Search API Indexes	Lists all indexes with ID, name, server, status, datasources, field count, and tracker statistics. Optionally filter by server.
6	get_index	Get Search API Index	Gets comprehensive details about an index including fields, processors, datasources, tracker stats, and options.
7	create_index	Create Search Index	Creates a new index on a server with datasources. Configure bundles to restrict which content types are indexed.
8	update_index	Update Search Index	Updates settings on an existing index such as name, description, server, status, and options like cron_limit and index_directly.
9	delete_index	Delete Search Index	Deletes an index and all its indexed data. This action cannot be undone.
10	manage_index_fields	Manage Index Fields	Add, remove, or list available fields on an index. Supports actions: add, remove, list_available.
11	manage_index_processors	Manage Index Processors	Add, remove, or list processors on an index. Supports actions: add, remove, list_available, list_enabled.
12	index_operations	Index Operations	Perform indexing operations: status, reindex (mark all for re-indexing), clear (delete indexed data), or index_now (index a batch immediately).
13	enable_search_backend	Enable Search Backend	Enables a search backend module (search_api_db, search_api_solr, or elasticsearch_search_api).

Common Processors

Processor ID	Purpose
content_access	Restricts search results based on user permissions (recommended for most indexes)
html_filter	Strips HTML tags from indexed content
ignorecase	Makes searches case-insensitive
tokenizer	Splits text into individual words for indexing
stemmer	Reduces words to their root form (running -> run)
stopwords	Removes common words (the, a, is, etc.)
transliteration	Converts special characters to ASCII equivalents
rendered_item	Indexes the rendered output of entities
add_url	Adds the entity URL to indexed data
aggregated_field	Creates virtual fields combining multiple source fields
highlight	Highlights search terms in results

Default Field Recommendations

For content indexes, these fields provide a solid starting point:

Field	Type	Notes
title	text	Node title for full-text search (boost 5.0 recommended)
body	text	Body/content field for full-text search
created	date	Creation date for sorting and filtering
changed	date	Last modified date
type	string	Content type for filtering/faceting
uid	integer	Author for filtering
status	boolean	Published status
sticky	boolean	Sticky flag for boosting

Default Backend Configuration

For the search_api_db backend:

• min_chars: 1 (minimum word length to index)
• matching: partial (allows partial word matching)

AI Search / Vector Search

The AI Search backend (search_api_ai_search_backend) from the ai_search module enables:

• Vector embeddings for semantic search
• RAG (Retrieval Augmented Generation) capabilities
• Similarity-based search instead of keyword matching
• Integration with AI providers for embedding generation

Recommended Setup Workflow

Follow this order when building search infrastructure from scratch:

Step 1: Enable a backend

Check available backends with list_servers or enable one with enable_search_backend. Start with search_api_db for simple setups that need no extra infrastructure.

Step 2: Create a server

Create a server with the desired backend. For database backend, sensible defaults are applied if no backend config is provided.

Step 3: Create an index

Create an index on that server, selecting datasources (e.g. entity:node). Optionally restrict to specific bundles (e.g. only article and page content types).

Step 4: Add fields

Add fields to the index. Start with title, body, created date, content type, author, and status. Set appropriate types (text for full-text search, string for exact match/facets, date for date fields).

Step 5: Add processors

Add processors for search quality. Common defaults: content_access, html_filter, ignorecase, tokenizer.

Step 6: Run initial indexing

Use index_operations with the index_now operation to index a batch of items, or reindex to mark everything for indexing on the next cron run.

Safety Warnings

• Deleting a server will disconnect all its indexes
• Clearing an index removes all indexed data (requires full re-indexing)
• Reindexing marks all items for re-indexing but does not delete existing data
• Adding/removing processors may require reindexing
• Large index_now operations may time out -- use reasonable batch sizes (50-100)
• Always check for connected indexes before deleting a server

Example Workflows

View current search configuration

"List all Search API servers"
"Show me all search indexes and their status"
"What fields are indexed on the content index?"

Set up a basic database search

"Create a database search server"
"Create a content search index on the database server"
"Add the title, body, created, and type fields to the index"
"Enable the HTML filter, ignore case, and tokenizer processors"
"Index all items now"

Set up a full-text article search

"Set up an index for articles and pages with title, body, and tags fields"
"Configure the stemmer processor for English language"
"Enable content access processor for secure search results"

Complete infrastructure in one request

"Set up a complete search infrastructure: create a database server, content index
with title/body/tags/author fields, and enable HTML filter, ignore case, and
tokenizer processors"

Manage fields and processors

"Add the body field to the content index"
"Remove the sticky field from the content index"
"Add the ignore case and tokenizer processors"
"List available processors for the content index"

Indexing operations

"Check the indexing status of all indexes"
"Reindex all items on the content index"
"Clear the content index and rebuild it"