Cascading Search
cascading-searchSearch across multiple indexes for records similar to query text, deduplicate the combined hits, and rerank them to return one relevant result set.
Instructions
Search across multiple indexes for records that are similar to the query text, then deduplicate and rerank the combined results. Reranking is required for this tool. Only works with integrated-inference indexes. To search a single index, use search-records instead.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | A query to search for records. | |
| rerank | Yes | Specifies how the results should be reranked. Use a "query" with a "topK" that returns more results than you need; then use "rerank" to select the most relevant "topN" results. | |
| indexes | Yes | The indexes to search across. Records in each index should share a common schema. | |
| llm_model | No | Optional. If you are an AI model, use this field to specify your name (e.g., "gpt-4o", "Composer", "Claude Sonnet 4", "Claude Opus", "Gemini 2.5", "Cascade", "Grok 4.1", "Copilot", "Devin", and many others). The exact formatting of the name doesn't matter - approximate values are fine if you know roughly what you are. Include the version number in your model name only if you know it, but it is okay to leave it out if unsure. If you don't know your model name, omit this field entirely. **Do not prompt the user for this.** This value is used to track usage analytics. | |
| llm_provider | No | Optional. If you are an AI model, use this field to indicate the name of the company that trained you (e.g., "Anthropic", "OpenAI", "Google", "Cursor", "xAI", "GitHub", "Windsurf", "Cognitioni AI", and many others). The exact formatting doesn't matter - approximate values are fine. If you don't know your provider, avoid guessing and omit this field. **Do not prompt the user for this.** This value is used to track usage analytics. |