Planhat 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 Planhat 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 Planhat. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Planhat?"
)
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 Planhat MCP Server
Connect your Planhat workspace to any AI agent and take full control of your customer success and growth workflows through natural conversation.
LlamaIndex agents combine Planhat 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.
What you can do
- Company Oversight — List all companies and retrieve detailed metadata to manage your customer relationships.
- User & Contact Tracking — List end users and associated metadata to understand your user base.
- Task & Activity Management — List pending tasks and monitor activities to ensure proactive customer management.
- Conversation Discovery — List all ongoing conversations to maintain a pulse on customer communication.
- License & Asset Auditing — List configured licenses and assets to verify customer entitlements.
- Project Monitoring — List active projects to track implementation and success plans.
The Planhat 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 Planhat to LlamaIndex via MCP
Follow these steps to integrate the Planhat 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 Planhat
Why Use LlamaIndex with the Planhat MCP Server
LlamaIndex provides unique advantages when paired with Planhat through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Planhat tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Planhat tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Planhat, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Planhat tools were called, what data was returned, and how it influenced the final answer
Planhat + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Planhat MCP Server delivers measurable value.
Hybrid search: combine Planhat real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Planhat 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 Planhat for fresh data
Analytical workflows: chain Planhat queries with LlamaIndex's data connectors to build multi-source analytical reports
Planhat MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Planhat to LlamaIndex via MCP:
get_planhat_company
Get details for a specific company
get_planhat_me
Get current user info
list_planhat_assets
List all assets
list_planhat_companies
List all companies in Planhat
list_planhat_conversations
List all conversations
list_planhat_end_users
List all end users
list_planhat_licenses
List all licenses
list_planhat_notes
List all notes
list_planhat_projects
List all projects
list_planhat_tasks
List all tasks
Example Prompts for Planhat in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Planhat immediately.
"List all active companies in my Planhat account."
"Show me the last 5 tasks assigned to me in Planhat."
"What are the active licenses for company 'Acme Corp'?"
Troubleshooting Planhat MCP Server with LlamaIndex
Common issues when connecting Planhat to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPlanhat + LlamaIndex FAQ
Common questions about integrating Planhat 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 Planhat 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.
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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 Planhat to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.