Vectara MCP server

Run a RAG query using Vectara, returning search results with a generated response. Args: query: str, The user query to run - required. corpus_keys: list[str], List of Vectara corpus keys to use for the search - required. Please ask the user to provide one or more corpus keys. api_key: str, The Vectara API key - required. n_sentences_before: int, Number of sentences before the answer to include in the context - optional, default is 2. n_sentences_after: int, Number of sentences after the answer to include in the context - optional, default is 2. lexical_interpolation: float, The amount of lexical interpolation to use - optional, default is 0.005. max_used_search_results: int, The maximum number of search results to use - optional, default is 10. generation_preset_name: str, The name of the generation preset to use - optional, default is "vectara-summary-table-md-query-ext-jan-2025-gpt-4o". response_language: str, The language of the response - optional, default is "eng". Returns: The response from Vectara, including the generated answer and the search results.

{ "properties": { "api_key": { "default": "", "title": "Api Key", "type": "string" }, "corpus_keys": { "default": [], "items": { "type": "string" }, "title": "Corpus Keys", "type": "array" }, "generation_preset_name": { "default": "vectara-summary-table-md-query-ext-jan-2025-gpt-4o", "title": "Generation Preset Name", "type": "string" }, "lexical_interpolation": { "default": 0.005, "title": "Lexical Interpolation", "type": "number" }, "max_used_search_results": { "default": 10, "title": "Max Used Search Results", "type": "integer" }, "n_sentences_after": { "default": 2, "title": "N Sentences After", "type": "integer" }, "n_sentences_before": { "default": 2, "title": "N Sentences Before", "type": "integer" }, "query": { "title": "Query", "type": "string" }, "response_language": { "default": "eng", "title": "Response Language", "type": "string" } }, "required": [ "query" ], "title": "ask_vectaraArguments", "type": "object" }