How to Use the Indy MCP in LlamaIndex

Turn your LlamaIndex knowledge base into a freelance engine by indexing Indy data for semantic search and retrieval.

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Connect Indy MCP to LlamaIndex

Create your Vinkius account to connect Indy 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 Indy with LlamaIndex

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Index Indy records in LlamaIndex

Use `list_records` to pull your business history into a vector store. You can search across past client inquiries and project files using natural language. Your index becomes a unified source of truth. You retrieve specific contract or form submission details just by asking your agent a question.

Ground AI answers with Indy data

Call `get_form` or `get_user` to provide context for your RAG pipeline. Your agent answers questions about project status by referencing live data from your account. This stops your agent from guessing. It pulls exact values from your records to ensure every answer is based on your real business information.

Manage business workflows via Indy

Register hooks with `create_webhook` to update your vector index whenever a new record appears. Your knowledge base stays current without manual re-indexing. Your agent maintains a perfect view of your freelance operations. It stays aware of every new lead or signed agreement as it happens.

Setup guide

Set up Indy 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 Indy 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 Indy tools.",
)
response = await agent.run("List recent Indy 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 Indy. 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 Indy MCP in LlamaIndex

The server converts API responses into searchable documents. You can then query these documents to find specific project or client information.

Yes. Once you list your forms, the data is indexed for semantic retrieval. You can find forms based on their content or purpose.

Yes. The server provides direct access to your records, which are then cached in your vector store for low-latency querying.

All requests are authenticated via your endpoint token. Only your agent can access the records you explicitly permit through the tool list.

The server manages form submissions, user profiles, and account status records. This data is kept private and used only for your local agent's operations.

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