Fellow MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Check Fellow Status, Complete Action Item, Create Action Item, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Fellow as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Fellow app connector for LlamaIndex is a standout in the Productivity category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Fellow. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Fellow?"
)
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 Fellow MCP Server
Connect your Fellow workspace to any AI agent and manage your entire meeting workflow through natural conversation.
LlamaIndex agents combine Fellow tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through 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
- Meetings — List and inspect meetings with titles, participants, dates, and agendas.
- Notes — Access structured meeting notes and AI-generated summaries.
- Action Items — Create, track, and complete action items with assignees and due dates.
- Streams — Browse recurring meeting series and their schedules.
- Users — List all workspace members and their roles.
The Fellow MCP Server exposes 12 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.
All 12 Fellow tools available for LlamaIndex
When LlamaIndex connects to Fellow through Vinkius, your AI agent gets direct access to every tool listed below — spanning meeting-management, collaborative-agendas, action-items, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Verify Fellow API connectivity
Mark an action item as completed
Optionally link it to a meeting, assign to a user by email, and set a due date. Create a new action item from a meeting
Get details of a specific action item
Get full details of a specific meeting
Get full content of a specific note
Get details of a specific meeting stream
Optionally filter by status: "pending", "completed", or "archived". List action items from meetings
List recent meetings from Fellow
Optionally filter by a specific meeting ID to get notes for that meeting only. List meeting notes
List all meeting streams (recurring series)
List all users in the Fellow workspace
Connect Fellow to LlamaIndex via MCP
Follow these steps to wire Fellow into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Fellow MCP Server
LlamaIndex provides unique advantages when paired with Fellow through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Fellow tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Fellow tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Fellow, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Fellow tools were called, what data was returned, and how it influenced the final answer
Fellow + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Fellow MCP Server delivers measurable value.
Hybrid search: combine Fellow real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Fellow 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 Fellow for fresh data
Analytical workflows: chain Fellow queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Fellow in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Fellow immediately.
"Show me my recent meetings in Fellow."
"Create an action item 'Send proposal to client' and assign it to sarah@team.com with a due date of May 10."
"List all pending action items."
Troubleshooting Fellow MCP Server with LlamaIndex
Common issues when connecting Fellow to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFellow + LlamaIndex FAQ
Common questions about integrating Fellow MCP Server with LlamaIndex.