How do I find my Fibery Workspace name?

Your workspace name is the subdomain in your Fibery URL. For example, if you access Fibery at `https://my-team.fibery.io`, your workspace name is `my-team`.

How do I generate a Fibery API Token?

In your Fibery workspace, go to **Settings > API**, click on **Create New Token**, give it a name, and copy the generated token immediately.

Can I query custom fields created in my spaces?

Yes! Use the `get_schema` tool to see the fully qualified names of your custom fields, and then include them in the `select_json` parameter of the `query_entities` tool.

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

Name: Fibery
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 Fibery 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({
        "fibery": {
            "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 Fibery, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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 Fibery MCP Server

Fibery is a work management platform that adapts to your unique processes. This MCP server allows your AI agent to interact with your Fibery workspace seamlessly.

LangChain's ecosystem of 500+ components combines seamlessly with Fibery 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.

Key Features

Space & Schema Discovery — List all your spaces (apps) and retrieve the full schema to understand your custom databases and fields.
Entity Management — Query, create, update, and delete entities across any of your custom databases flawlessly.
Comment Integration — Read and add comments to entities to keep your team in sync natively.
Advanced Querying — Use granular filters and field selections to retrieve exactly the data you need synchronously.
Cross-Database Search — Search for information across your entire workspace flawlessly through the agent.

The Fibery 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 Fibery to LangChain via MCP

Follow these steps to integrate the Fibery 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 Fibery via MCP

Why Use LangChain with the Fibery MCP Server

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

The largest ecosystem of integrations, chains, and agents — combine Fibery 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 Fibery queries for multi-turn workflows

Fibery + LangChain Use Cases

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

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

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

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

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

Fibery MCP Tools for LangChain (11)

These 11 tools become available when you connect Fibery to LangChain via MCP:

add_comment

Add a comment to an entity

create_entity

Create a new entity in a specific database

delete_entity

Delete an entity

get_comments

Retrieve comments for a specific entity

get_entity

Get a specific entity by its UUID

get_schema

Retrieve the full schema of the workspace, including all databases (types) and fields

list_apps

List all Fibery apps (spaces)

list_users

List all users in the Fibery workspace

query_entities

Query entities from a specific database (type)

search_entities

Search for entities by keyword across all databases

update_entity

Update an existing entity

Example Prompts for Fibery in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Fibery immediately.

"List all active spaces in my Fibery account."

"Show me the tasks assigned to me in the 'Software Development' space."

"Add a comment to task UUID-123 saying 'The client approved the design'."

Troubleshooting Fibery MCP Server with LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Fibery + LangChain FAQ

Common questions about integrating Fibery 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.