JobNimbus 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 JobNimbus 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 JobNimbus. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in JobNimbus?"
)
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 JobNimbus MCP Server
Empower your AI agents with JobNimbus's specialized CRM for contractors. This MCP server allows you to list and retrieve contacts and jobs, manage tasks and workflows, track payments, and view organization users directly through the JobNimbus API. Ideal for automating field service operations and project management.
LlamaIndex agents combine JobNimbus 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 JobNimbus 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 JobNimbus to LlamaIndex via MCP
Follow these steps to integrate the JobNimbus 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 JobNimbus
Why Use LlamaIndex with the JobNimbus MCP Server
LlamaIndex provides unique advantages when paired with JobNimbus through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine JobNimbus tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain JobNimbus tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query JobNimbus, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what JobNimbus tools were called, what data was returned, and how it influenced the final answer
JobNimbus + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the JobNimbus MCP Server delivers measurable value.
Hybrid search: combine JobNimbus real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query JobNimbus 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 JobNimbus for fresh data
Analytical workflows: chain JobNimbus queries with LlamaIndex's data connectors to build multi-source analytical reports
JobNimbus MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect JobNimbus to LlamaIndex via MCP:
get_contact
Returns addresses, phone numbers, email, and custom fields. Use this for deep intelligence on a customer before an interaction. Retrieves details for a specific contact
get_job
Returns project descriptions, associated contact IDs, and current workflow status. Use this to analyze project specifics or provide an update on a job. Retrieves details for a specific job
list_boards
Useful for navigating the account structure. Lists all configured boards
list_contacts
Returns names, contact types, and IDs. Use this to identify clients or start a search for a specific customer. Lists all contacts in JobNimbus
list_jobs
Includes job titles, status, and IDs. Essential for monitoring project flow and upcoming work. Lists all jobs in JobNimbus
list_payments
Essential for monitoring revenue and project billing status. Lists all recent payments
list_products
Useful for auditing available services and pricing items. Lists all products and services
list_tasks
Returns task descriptions, due dates, and IDs. Use this to help the user manage their daily workload or audit team activities. Lists all tasks
list_users
Useful for identifying sales reps or project managers. Lists all users in the organization
list_workflows
Useful for understanding the steps in the company's business processes. Lists all configured workflows
Example Prompts for JobNimbus in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with JobNimbus immediately.
"List all active contacts in JobNimbus."
"Show me the latest jobs created."
"Check the status of my active tasks."
Troubleshooting JobNimbus MCP Server with LlamaIndex
Common issues when connecting JobNimbus to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpJobNimbus + LlamaIndex FAQ
Common questions about integrating JobNimbus 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 JobNimbus 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 JobNimbus to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.