Float 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 Float 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 Float. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Float?"
)
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 Float MCP Server
Connect your Float account to any AI agent and automate your resource management and team scheduling through the Model Context Protocol (MCP). Float is the leading resource planning platform that helps agencies and teams keep track of who is working on what and when. Now, you can manage allocations, check availability, and oversee project timelines directly through natural conversation.
LlamaIndex agents combine Float tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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
- Team Scheduling — List all team members and fetch detailed availability and profile metadata.
- Project Oversight — Access active projects, retrieve specific project details, and manage the team members assigned to them.
- Task Allocations — Create and list project allocations, assigning specific hours and dates to team members instantly.
- Time Off Management — Monitor scheduled vacations, sick leave, and public holidays to ensure accurate capacity planning.
- Logged Time Analysis — Retrieve actual hours worked versus scheduled time to track project progress and efficiency.
- Organization Discovery — List clients, departments, and account users to maintain full context of your agency's structure.
- Capacity Planning — Fetch high-level snapshots of team utilization and task labels (e.g., Design, Development).
The Float 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 Float to LlamaIndex via MCP
Follow these steps to integrate the Float 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 Float
Why Use LlamaIndex with the Float MCP Server
LlamaIndex provides unique advantages when paired with Float through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Float tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Float tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Float, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Float tools were called, what data was returned, and how it influenced the final answer
Float + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Float MCP Server delivers measurable value.
Hybrid search: combine Float real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Float 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 Float for fresh data
Analytical workflows: chain Float queries with LlamaIndex's data connectors to build multi-source analytical reports
Float MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Float to LlamaIndex via MCP:
create_allocation
Schedule a task
get_logged_time
Get actual hours
get_person
Get person details
get_project
Get project details
list_allocations
List task allocations
list_clients
List clients
list_departments
List departments
list_people
List team members
list_project_task_names
g. Design, Dev). List task labels
list_projects
List projects
list_time_offs
List time off
list_user_accounts
List user accounts
Example Prompts for Float in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Float immediately.
"List all active projects in Float and the team members assigned to them."
"Schedule John Doe for 4 hours a day on the 'Q3 Marketing' project from Monday to Friday."
"Who is scheduled for time off this month?"
Troubleshooting Float MCP Server with LlamaIndex
Common issues when connecting Float to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFloat + LlamaIndex FAQ
Common questions about integrating Float 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 Float 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 Float to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.