Fibery 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 Fibery as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Fibery. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in Fibery?"
)
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 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.
LlamaIndex agents combine Fibery 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.
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 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 Fibery to LlamaIndex via MCP
Follow these steps to integrate the Fibery 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 Fibery
Why Use LlamaIndex with the Fibery MCP Server
LlamaIndex provides unique advantages when paired with Fibery through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Fibery tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Fibery tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Fibery, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Fibery tools were called, what data was returned, and how it influenced the final answer
Fibery + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Fibery MCP Server delivers measurable value.
Hybrid search: combine Fibery real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Fibery 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 Fibery for fresh data
Analytical workflows: chain Fibery queries with LlamaIndex's data connectors to build multi-source analytical reports
Fibery MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect Fibery to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Fibery to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFibery + LlamaIndex FAQ
Common questions about integrating Fibery 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 Fibery 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 Fibery to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.