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.

Run a semantic search query using Vectara, without generation. 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. Returns: The response from Vectara, including the matching search results.