Jibble 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 Jibble as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Jibble. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Jibble?"
)
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 Jibble MCP Server
Empower your AI agents with Jibble's time tracking and attendance platform. This MCP server allows you to list time entries, retrieve person details, track activities and projects, and view organization information directly through the Jibble API. Ideal for automating workforce management and productivity analysis.
LlamaIndex agents combine Jibble 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 Jibble 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 Jibble to LlamaIndex via MCP
Follow these steps to integrate the Jibble 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 Jibble
Why Use LlamaIndex with the Jibble MCP Server
LlamaIndex provides unique advantages when paired with Jibble through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Jibble tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Jibble tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Jibble, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Jibble tools were called, what data was returned, and how it influenced the final answer
Jibble + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Jibble MCP Server delivers measurable value.
Hybrid search: combine Jibble real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Jibble 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 Jibble for fresh data
Analytical workflows: chain Jibble queries with LlamaIndex's data connectors to build multi-source analytical reports
Jibble MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Jibble to LlamaIndex via MCP:
get_organization
Use to verify account-wide configuration. Retrieves organization details
get_person
Essential for detailed HR analysis of an individual team member. Retrieves details for a specific person
get_time_entry
Returns location data, activity notes, and associated device info. Use for auditing or correcting a specific employee time log. Retrieves details for a specific time entry
list_activities
g., "Meeting", "Development", "Break") that employees can select when clocking in. Useful for identifying high-level task categories. Lists all configured activities
list_clients
Useful for professional services tracking and billable hours auditing. Lists all configured clients
list_groups
g., "Sales Team", "Remote Workers") used to organize the workforce. Useful for group-based performance reporting. Lists all configured groups
list_locations
Useful for auditing site-based workforce distribution. Lists all configured locations
list_people
Includes names, emails, and internal IDs. Use this to identify personnel before querying their time entries. Lists all people in the organization
list_projects
Use this when the user asks for a project-based time breakdown. Lists all configured projects
list_time_entries
Returns employee IDs, entry times, and durations. Use this to monitor workforce activity and total work hours. Lists all time entries
Example Prompts for Jibble in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Jibble immediately.
"List all people in my Jibble organization."
"Show me the recent time entries."
"What are the active projects in Jibble?"
Troubleshooting Jibble MCP Server with LlamaIndex
Common issues when connecting Jibble to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpJibble + LlamaIndex FAQ
Common questions about integrating Jibble 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 Jibble 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 Jibble to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.