Algolia mcp

Algolia Search & Analytics

Need to write/modify data? Use algolia-cli instead — it handles imports, exports, backups, settings changes, synonyms, rules, API keys, and all admin operations.

Connection setup

Use /algolia-mcp:mcp-connect to configure the MCP client with the Algolia MCP server. For manual client setup, see connection-setup.

Tool selection

Search

Task	Tool
Search an index	algolia_search_index
List available indices	algolia_search_list_indices
Explore facet values	algolia_search_for_facet_values

Analytics

Task	Tool
Top searches	algolia_analytics_top_searches
Searches with no results	algolia_analytics_searches_no_results
No-results rate	algolia_analytics_no_results_rate
Click positions	algolia_analytics_click_positions
No-click rate	algolia_analytics_no_click_rate
Searches without clicks	algolia_analytics_top_searches_without_clicks
Search volume	algolia_analytics_number_of_searches
Top search results	algolia_analytics_top_search_results
Unique users	algolia_analytics_number_of_users
Top filters	algolia_analytics_top_filters
Filters with no results	algolia_analytics_top_filters_no_results
Top countries	algolia_analytics_top_countries

Recommendations

Task	Tool	model parameter
Frequently bought together	algolia_recommendations	bought-together
Related products	algolia_recommendations	related-products
Trending items	algolia_recommendations	trending-items
Trending facets	algolia_recommendations	trending-facets
Visually similar items	algolia_recommendations	looking-similar

Search Filter Syntax

Filters go in the algolia_search_index call alongside query:

facetFilters (array-based):

[["color:red", "color:blue"]]              → OR (red OR blue)
[["brand:Nike"], ["category:running"]]     → AND (Nike AND running)
[["size:10"], ["color:red", "color:blue"]] → mixed (size 10 AND (red OR blue))

Each inner array is OR'd; outer arrays are AND'd.

numericFilters (string-based):

["price < 100"]                    → single condition
["price >= 50", "price <= 200"]    → range (AND'd)

Date filtering: Dates must be stored as Unix timestamps. Use numericFilters: ["timestamp >= 1704067200"].

Attribute selection: Use attributesToRetrieve: ["name", "price"] to limit response size.

Analytics Key Details

• clickAnalytics: true: Set this on algolia_analytics_top_searches or algolia_analytics_top_search_results to include CTR, conversion rate, and click count. Only these two tools support it.
• revenueAnalytics: true: Set on the same tools to also include add-to-cart rate, purchase rate, and revenue.
• Data delay: Recent data has a 1–4 hour processing delay. Use date ranges ending at least 4 hours ago for complete data.

Interpreting Results

No-results rate	Assessment
< 5%	Excellent
5–10%	Good
10–20%	Needs improvement
> 20%	Poor

Click positions: Healthy = 30–40% of clicks at position 1, decreasing through 10. Even distribution = poor relevance. Concentrated at positions 5–10 = ranking issues.

Low CTR + high search volume = poor result relevance. Common causes: missing synonyms, content gaps, mismatched query intent.

Recommendation Thresholds

Threshold	Behavior
50	More results, lower relevance
60	Balanced (good default)
75	Fewer results, higher relevance

Model parameter requirements:

• bought-together, related-products, looking-similar → require objectID
• trending-items → does NOT require objectID. Use facetName + facetValue to filter by category
• trending-facets → requires facetName

Required Workflow

1. Discover first: Always call algolia_search_list_indices before other tools to resolve applicationId and indexName. The applicationId parameter is an enum — select from the values in the tool schema, never guess.
2. Index names are case-sensitive: Use the exact name returned by algolia_search_list_indices.
3. Date parameters: Analytics tools accept startDate and endDate in YYYY-MM-DD format. Default period is the last 8 days.
4. Permissions: Not all tools are available to every user. Analytics tools require the Analytics permission; recommendations require the Recommend feature.

Common Workflows

Search Quality Audit

1. algolia_search_list_indices → get applicationId and index name
2. algolia_analytics_no_results_rate → check overall health (< 5% is excellent)
3. algolia_analytics_searches_no_results → find the specific failing queries
4. algolia_analytics_top_searches with clickAnalytics: true → find high-volume queries with low CTR
5. algolia_analytics_click_positions → check if clicks are concentrated at position 1 (good) or spread evenly (poor relevance)
6. For each problematic query: algolia_search_index with that query to see what results look like

Recommendation Setup Check

1. algolia_search_list_indices → resolve applicationId
2. Start with trending-items (requires least data) to verify Recommend is working
3. Then try bought-together or related-products with a known product objectID
4. If results are empty, check event volume requirements in recommendations reference

Reference Docs

• connection-setup — MCP server configuration and authentication
• search — Search parameters, filter syntax (facetFilters, numericFilters), pagination
• analytics — Analytics metrics interpretation, date ranges, click/conversion tracking
• recommendations — Recommendation models, thresholds, facet-based filtering
• troubleshooting — Common errors and resolution steps