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When should you use this server

Use Vectara MCP when you want an agent to:
  • Run semantic search over enterprise documents.
  • Perform RAG queries that return both search results and a synthesized answer.
  • Ground responses in organization-specific data with minimal setup.

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Tools provided

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ask_vectara

Run a RAG query using Vectara. Returns search results along with a generated response. Arguments
  • query (string, required) — the user query to run.
  • corpus_keys (list[string], required) — corpus keys to search; the user must provide one or more.
  • api_key (string, required) — the Vectara API key.
  • n_sentences_before (int, optional, default=2) — sentences before a hit to include in context.
  • n_sentences_after (int, optional, default=2) — sentences after a hit to include in context.
  • lexical_interpolation (float, optional, default=0.005) — balance between semantic and lexical match.
  • max_used_search_results (int, optional, default=10) — max number of search results to use.
  • generation_preset_name (string, optional, default=vectara-summary-table-md-query-ext-jan-2025-gpt-4o) — generation preset.
  • response_language (string, optional, default=eng) — language of the generated response.
Returns
  • Generated answer (string)
  • Supporting search results (list of documents/snippets)
Example usage
  • “Summarize the refund policy from the Finance corpus.”
  • “Answer FAQs about GDPR using the Compliance corpus.”

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search_vectara

Run a semantic search query without generation. Returns only the most relevant search results. Arguments
  • query (string, required) — the user query to run.
  • corpus_keys (list[string], required) — corpus keys to search.
  • api_key (string, required) — the Vectara API key.
  • n_sentences_before (int, optional, default=2) — sentences before a hit to include.
  • n_sentences_after (int, optional, default=2) — sentences after a hit to include.
  • lexical_interpolation (float, optional, default=0.005) — balance between semantic and lexical match.
Returns
  • Matching search results with context snippets.
Example usage
  • “Find all product docs mentioning SAML login.”
  • “Search engineering notes for ‘distributed caching.’”
Source
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