Ejento AI
GuidesQuickstartRecipes
REST APIsRelease NotesFAQs
GuidesQuickstartRecipes
REST APIsRelease NotesFAQs
Ejento AI
  1. Recipes
  • Introduction
  • LlamaCloud Connector
  • General
    • Employee Engagement With AI HR Assistant
    • Automate Blog Writing With AI Assistant
    • Customer Support With AI Assistants
  • Azure
    • Azure AI Search
    • Azure Cosmos DB
  • Snowflake
    • Snowflake
  • Google Drive
  1. Recipes

LlamaCloud Connector

LlamaCloud MCP Server#

The LlamaCloud MCP Server connects your LlamaCloud knowledge base to Ejento as a tool. Once set up, Ejento can search and retrieve content from your indexed documents in real time — no hardcoded data, no retraining. Just point it at your index and ask questions in natural language.

Prerequisites#

Before deploying this MCP server, make sure you have the following ready:
A LlamaCloud account at cloud.llamaindex.ai
A LlamaCloud index already created with at least one document uploaded and indexed
A LlamaCloud API key from https://cloud.llamaindex.ai/api-key
Your project name (visible in the LlamaCloud UI, defaults to Default)
Note: The index must exist and contain documents before the server starts. The server connects to an existing index — it does not create one.

Configuration & Connecting With Ejento#

Set the following environment variables in the web app.
VariableRequiredDescription
LLAMA_CLOUD_API_KEY✅Your LlamaCloud API key
LLAMA_CLOUD_INDEX_NAME✅The name of your LlamaCloud index
LLAMA_CLOUD_PROJECT_NAME✅The project the index belongs to (usually Default)
LLAMA_CLOUD_ORG_IDoptionalOrganization ID — only needed if you have multiple orgs

Once configured, you can start using the connector on Ejento
ejento.png

Available Tools#

query_index#

The primary tool. Accepts a natural language question and returns relevant content retrieved from the index. Use this for any question that could be answered from your indexed documents.
Example: "What are the refund policies described in the document?"

list_documents#

Returns a list of all documents currently in the index, including their names and IDs. Useful for confirming what has been indexed before querying.
Example: "What documents are in the index?"

insert_document#

Adds a new piece of text content to the index at runtime. Requires a text body and a unique doc_id.
Example: "Add this new policy update to the index."

delete_document#

Removes a document from the index by its ID. Use list_documents first to confirm the correct ID before deleting.
Example: "Delete the document with ID doc-001 from the index."

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