How to Use the Feathery MCP in LlamaIndex

Index your Feathery form data into LlamaIndex for grounded, searchable AI knowledge.

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Connect Feathery MCP to LlamaIndex

Create your Vinkius account to connect Feathery 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 Feathery with LlamaIndex

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Build a knowledge base with LlamaIndex

Run `list_forms` and `get_form_details` to ingest your configuration into a vector store. LlamaIndex indexes this output, making your form logic queryable by natural language. This means you stop guessing how a form is structured. You just ask your agent, and it pulls the schema directly from your indexed Feathery data.

Search past form sessions

Index historical session data from `get_form_session` to help your agent answer questions about specific user interactions. It grounds AI answers in actual, indexed API records. No more hallucinations about form inputs. Your agent retrieves the exact values stored for a user and uses that context to answer your prompts.

Query workflow logic instantly

Fetch your automated workflow definitions using `list_workflows` and index them. Your agent gains a searchable map of your business logic. It lets you ask how a specific workflow behaves under certain conditions. You get answers based on your actual live configuration, not documentation that might be outdated.

Setup guide

Set up Feathery 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 Feathery 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 Feathery tools.",
)
response = await agent.run("List recent Feathery 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 Feathery. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Feathery MCP in LlamaIndex

You ingest the tool outputs directly into your index. This allows Feathery data to become a searchable part of your RAG application.

Absolutely. Once you index the output of the tools, your agent can perform semantic searches across all your form data.

You control the index. The MCP server only provides the data you request, and you decide how that data is stored locally.

Your agent just runs the tool again to refresh the index. It keeps your knowledge base current with your latest form definitions.

It accesses user field values as requested. All data transmitted is scoped to your specific API token and handled via secure transport.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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