vectara/vectara-mcp
by Various
Open source MCP server for Vectara
MCP
vectara/vectara-mcp
Added 1 June 2026
Overview
An open-source MCP (Model Context Protocol) server that bridges Vectara's retrieval-augmented generation platform with MCP-compatible AI agents. It is written in Python and exposes Vectara's search and indexing capabilities as standard MCP tools.
Best for
Best for
Developers who use Vectara and want to connect it to MCP-compatible AI agents or tools
Use cases
- Connect an MCP-compatible AI agent to query a Vectara corpus
- Index documents into Vectara from within an agent workflow
- Build retrieval-augmented generation pipelines via the MCP protocol
How to use
Install
pip install vectara-mcp Tools exposed
setup_vectara_api_keyclear_vectara_api_keyask_vectarasearch_vectaracorrect_hallucinationseval_factual_consistency
Tested with
Claude Desktop
Example client config
{\n "mcpServers": {\n "Vectara": {\n "command": "python",\n "args": ["-m", "vectara_mcp", "--stdio"],\n "env": {\n "VECTARA_API_KEY": "your-api-key"\n }\n }\n }\n} Notes
An open-source MCP (Model Context Protocol) server that bridges Vectara’s retrieval-augmented generation platform with MCP-compatible AI agents. It is written in Python and exposes Vectara’s search and indexing capabilities as standard MCP tools.
26 stars on GitHub. Last updated 2026-04-30. Licensed Apache-2.0.
Use cases
- Connect an MCP-compatible AI agent to query a Vectara corpus
- Index documents into Vectara from within an agent workflow
- Build retrieval-augmented generation pipelines via the MCP protocol
Pros
- Open-source with permissive licensing allows full customization
- Python-based, easy to extend or embed in existing Python projects
- Enables standardized integration between Vectara and many MCP clients
Cons
- Low star count (26) indicates early adoption and limited community support
- Documentation and usage examples may be sparse
- Tied to Vectara’s platform, not useful without an active Vectara account
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Open-source with permissive licensing allows full customization
- Python-based, easy to extend or embed in existing Python projects
- Enables standardized integration between Vectara and many MCP clients
Cons
- Low star count (26) indicates early adoption and limited community support
- Documentation and usage examples may be sparse
- Tied to Vectara's platform, not useful without an active Vectara account
Pairs with
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