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Glama
vectara

Vectara MCP server

Official
by vectara
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
VECTARA_API_KEYYesYour Vectara API key (required)
VECTARA_TRANSPORTNoDefault transport modehttp
VECTARA_AUTH_REQUIREDNoEnforce authenticationtrue
VECTARA_ALLOWED_ORIGINSNoAllowed origins for CORS (comma-separated)
VECTARA_AUTHORIZED_TOKENSNoAdditional auth tokens (comma-separated)

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
ask_vectaraA
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.
search_vectaraA
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.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.6/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: ask_vectara performs RAG queries with generated responses, while search_vectara performs semantic search without generation. Their descriptions explicitly differentiate between these functions, leaving no room for confusion or overlap in intended use.

Naming Consistency5/5

Both tools follow a perfect verb_noun pattern with 'vectara' as the consistent noun component. The naming is completely uniform across the tool set, using snake_case consistently without any deviations or mixed conventions.

Tool Count2/5

With only 2 tools, this server feels significantly under-scoped for a Vectara integration. A comprehensive MCP server for a search/RAG platform would typically include tools for corpus management, document operations, configuration settings, and additional query types beyond just these two basic search functions.

Completeness2/5

The tool surface is severely incomplete for a Vectara integration. While it covers basic querying, it lacks essential operations like creating/updating/deleting corpora, managing documents within corpora, configuring search parameters, handling authentication more robustly, or providing administrative functions. This creates significant gaps that will limit agent capabilities.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources