Feathery MCP Server for LlamaIndex 11 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Feathery as an MCP tool provider through the 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 Feathery. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in Feathery?"
)
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 Feathery MCP Server
Connect your Feathery.io account to any AI agent and take full control of your form automation and user data management through natural conversation.
LlamaIndex agents combine Feathery tool responses with indexed documents for comprehensive, grounded answers. Connect 11 tools through the 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
- User Orchestration — List all users in your environment and fetch detailed profiles including submission history natively
- Submission Intelligence — Retrieve granular field data submitted by specific users across all your automated forms flawlessly
- Session Monitoring — Query current form sessions to understand user progress and friction points in real-time
- Connector Auditing — List API connector logs to verify data synchronization and troubleshoot integration errors synchronously
- Form Management — List all active forms and retrieve structural details and metadata directly from the cloud
- Workflow Tracking — Inspect automated workflows and their execution status to ensure seamless user journeys
- Identity Context — Verify your API token user profile and account information through the agent flawlessly
The Feathery MCP Server exposes 11 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 Feathery to LlamaIndex via MCP
Follow these steps to integrate the Feathery 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 11 tools from Feathery
Why Use LlamaIndex with the Feathery MCP Server
LlamaIndex provides unique advantages when paired with Feathery through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Feathery tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Feathery tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Feathery, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Feathery tools were called, what data was returned, and how it influenced the final answer
Feathery + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Feathery MCP Server delivers measurable value.
Hybrid search: combine Feathery real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Feathery 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 Feathery for fresh data
Analytical workflows: chain Feathery queries with LlamaIndex's data connectors to build multi-source analytical reports
Feathery MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect Feathery to LlamaIndex via MCP:
get_account_info
Get Feathery account details
get_form_details
Get details for a specific form
get_form_session
Retrieve the current state/session of a specific form for a user
get_me
Get current API token identity info
get_user_data
Get all field values submitted by a specific user across forms
get_workflow_details
Get details for a specific workflow
list_connector_logs
List recent API connector error logs for a specific form
list_environments
List available Feathery environments
list_forms
List all forms in your Feathery account
list_users
List all users in your Feathery environment
list_workflows
List all automated workflows
Example Prompts for Feathery in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Feathery immediately.
"List all active forms in my account."
"Show me the data submitted by user user_99."
"Check if there are any connector errors for the Onboarding form."
Troubleshooting Feathery MCP Server with LlamaIndex
Common issues when connecting Feathery to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFeathery + LlamaIndex FAQ
Common questions about integrating Feathery 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 Feathery 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 Feathery to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.