Runn MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Runn as an MCP tool provider through the 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 Runn. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Runn?"
)
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 Runn MCP Server
Integrate your conversational AI natively with Runn, the premier real-time resource planning and forecasting platform. This integration enables your assistant to pull essential project metadata, capacity bottlenecks, people configurations, team allocations, and timesheet metrics directly into your sessions.
LlamaIndex agents combine Runn tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Analyze Projects & Resources — Extract ongoing engagement details, milestones, and client associations by querying lists natively (
list_projects,list_clients). Request detailed readouts of individual operational scopes (get_project). - Audit Roles & Assignments — Find exactly who is assigned to what phase, mapping active allocations accurately (
list_assignments,list_phases). Consult team members' details (list_people,get_person) or review globally defined roles (list_roles). - Review Budgets & Actuals — Safely extract reported operational logs (
list_actuals) to compare planned work versus billed hours. Account for non-working days naturally via the holidays lists (list_holidays).
The Runn MCP Server exposes 12 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 Runn to LlamaIndex via MCP
Follow these steps to integrate the Runn 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 12 tools from Runn
Why Use LlamaIndex with the Runn MCP Server
LlamaIndex provides unique advantages when paired with Runn through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Runn tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Runn tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Runn, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Runn tools were called, what data was returned, and how it influenced the final answer
Runn + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Runn MCP Server delivers measurable value.
Hybrid search: combine Runn real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Runn 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 Runn for fresh data
Analytical workflows: chain Runn queries with LlamaIndex's data connectors to build multi-source analytical reports
Runn MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Runn to LlamaIndex via MCP:
get_person
Retrieves details for a specific person
get_project
Retrieves details for a specific project
list_actuals
Lists actual hours logged (timesheet data)
list_assignments
Lists all resource assignments across projects
list_clients
Lists all clients in the organization
list_holidays
Lists public holidays and non-working days
list_milestones
Lists milestones for a specific project
list_people
Lists all people and resources in Runn
list_phases
Lists project phases for a specific project
list_projects
Lists all projects managed in Runn
list_roles
Lists all defined roles/positions
list_teams
Lists all teams in the workspace
Example Prompts for Runn in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Runn immediately.
"List all active projects mapped."
"Which team is assigned to the Alpha project next week?"
"What are the upcoming milestones for the Beta project?"
Troubleshooting Runn MCP Server with LlamaIndex
Common issues when connecting Runn to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpRunn + LlamaIndex FAQ
Common questions about integrating Runn 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 Runn 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 Runn to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.