Jestor MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Jestor as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Jestor. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Jestor?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Jestor MCP Server
Empower your AI agents with Jestor's low-code internal tools platform. This MCP server allows you to list objects (tables), retrieve and list records, manage users, and monitor workflows and dashboards directly through the Jestor API. Ideal for automating internal operations and database management.
LlamaIndex agents combine Jestor tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
The Jestor MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Jestor to LlamaIndex via MCP
Follow these steps to integrate the Jestor MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Jestor
Why Use LlamaIndex with the Jestor MCP Server
LlamaIndex provides unique advantages when paired with Jestor through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Jestor tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Jestor tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Jestor, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Jestor tools were called, what data was returned, and how it influenced the final answer
Jestor + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Jestor MCP Server delivers measurable value.
Hybrid search: combine Jestor real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Jestor to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Jestor for fresh data
Analytical workflows: chain Jestor queries with LlamaIndex's data connectors to build multi-source analytical reports
Jestor MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Jestor to LlamaIndex via MCP:
get_me
Use this to verify connection status and current permissions. Gets current authenticated user info
get_object
Useful for understanding field types and relationships within a specific table. Retrieves details/schema for a specific object
get_record
Essential for deep-diving into a specific entry in the database. Retrieves details for a specific record
list_apps
Useful for discovering high-level toolsets available to the user. Lists all installed internal apps
list_dashboards
Use this to identify where aggregated data visualizations are located. Lists all configured dashboards
list_objects
Returns object names and labels. Use this to discover available datasets before querying specific records. Lists all objects (tables) in your Jestor account
list_records
This is the primary tool for browsing data within a table (e.g., listing all "Tasks" or "Clients"). Lists records for a specific object
list_users
Returns names, emails, and IDs. Useful for identifying record owners or system administrators. Lists all users in the organization
list_webhooks
Use this to audit third-party integrations. Lists all configured webhooks
list_workflows
Useful for auditing system logic and event-driven actions. Lists all automated workflows
Example Prompts for Jestor in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Jestor immediately.
"List all objects in my Jestor account."
"Show me the records for the 'Invoices' object."
"Check the status of my workflows."
Troubleshooting Jestor MCP Server with LlamaIndex
Common issues when connecting Jestor to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpJestor + LlamaIndex FAQ
Common questions about integrating Jestor MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Jestor with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Jestor to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.