How do I obtain my Feathery API Key?

Log in to your Feathery dashboard, navigate to **Settings > API**, and generate a new key. Ensure you copy the token immediately as it is only shown once.

Can I see the data submitted by a specific user?

Yes! Use the `get_user_data` tool with the specific User ID to retrieve all field values submitted by that user across any of your forms.

How can I troubleshoot integration errors via chat?

The `list_connector_logs` tool retrieves recent error logs for your API connectors. This allows your agent to identify exactly which field mapping or endpoint caused a failure.

Feathery MCP Server for LangChain 11 tools — connect in under 2 minutes

Name: Feathery
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 11 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Feathery through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "feathery": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Feathery, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

LangChain's ecosystem of 500+ components combines seamlessly with Feathery through native MCP adapters. Connect 11 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Feathery MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 11 tools from Feathery via MCP

Why Use LangChain with the Feathery MCP Server

LangChain provides unique advantages when paired with Feathery through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine Feathery MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Feathery queries for multi-turn workflows

Feathery + LangChain Use Cases

Practical scenarios where LangChain combined with the Feathery MCP Server delivers measurable value.

RAG with live data: combine Feathery tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Feathery, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Feathery tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Feathery tool call, measure latency, and optimize your agent's performance

Feathery MCP Tools for LangChain (11)

These 11 tools become available when you connect Feathery to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

Common issues when connecting Feathery to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Feathery + LangChain FAQ

Common questions about integrating Feathery MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.