How to Use the LiquidPlanner Classic MCP in LlamaIndex

Q: Does LlamaIndex support filtering LiquidPlanner Classic tools?

Yes, you can use an allowedtools filter to limit which operations the agent can perform. This prevents unauthorized changes to your workspace.

Build RAG applications with LlamaIndex using your live project data from LiquidPlanner Classic.

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Connect LiquidPlanner Classic MCP to LlamaIndex

Create your Vinkius account to connect LiquidPlanner Classic to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup LiquidPlanner Classic with LlamaIndex

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Index project data for LlamaIndex

Transform your workspace info into a searchable knowledge base. Use `list_workspaces` to gather data that the system indexes for future queries. This makes your past project configurations semantically searchable. You get answers grounded in your actual API data rather than generic model knowledge.

Query your schedule via LlamaIndex

Ask questions about your timeline and get precise answers. The agent uses `get_task` to pull details and adds them to your knowledge index. This creates a unified view of your team's capacity. You can query across different projects to identify bottlenecks in real-time.

Combine docs and API data in LlamaIndex

Mix your internal documentation with live project metrics. The MCP Server provides the tools to pull the latest task lists while you search your local files. This approach gives your agent full context for decision making. It knows the difference between a static policy document and a changing project schedule.

Setup guide

Set up LiquidPlanner Classic MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all LiquidPlanner Classic MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to LiquidPlanner Classic tools.",
)
response = await agent.run("List recent LiquidPlanner Classic data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by LiquidPlanner Classic. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about LiquidPlanner Classic MCP in LlamaIndex

You index the tool outputs into a vector store. Once stored, you can run semantic queries against your project records to find specific task status updates.

Yes, you can use an allowed_tools filter to limit which operations the agent can perform. This prevents unauthorized changes to your workspace.

Absolutely, as the framework allows you to merge disparate data sources into one index. This provides a single point of truth for all your project information.

Yes, it pulls fresh data from the server when you trigger the tools. This ensures your RAG application is always working with current information.

Your project data is processed only within your local index instance. We ensure that task details and workspace IDs are never shared with external model providers.

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