How to Use the Fellow MCP in LlamaIndex

Q: Can I use LlamaIndex to query Fellow meeting transcripts?

Yes. Use gettranscript to retrieve the text, then index it into your vector store. Your agent can then search past conversations semantically.

Q: How do I avoid indexing outdated Fellow notes in LlamaIndex?

Set up a pipeline that checks the metadata from listnotes before indexing. You can filter out old or archived items to keep your vector index fresh.

Q: Does the Fellow MCP Server support parallel tool execution in LlamaIndex?

Yes. You can call tools like getactionitem and getnote asynchronously using totoollistasync to speed up your RAG queries.

Q: How do I filter which Fellow tools are exposed to LlamaIndex?

Use the allowedtools filter when setting up McpToolSpec. This lets you restrict the agent to read-only tools like listnotes if you want to avoid accidental edits.

Turn your Fellow meeting notes into a searchable knowledge base using the Fellow MCP Server with LlamaIndex.

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Connect Fellow MCP to LlamaIndex

Create your Vinkius account to connect Fellow to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Fellow with LlamaIndex

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Claude

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Index meeting notes for semantic search

Use `list_notes` and `get_note` to pull workspace documentation directly into your LlamaIndex vector store. Your agent can then run semantic queries across all past meetings instead of searching manually. This grounds your RAG applications in actual meeting history. It prevents the model from hallucinating details about decisions made during past calls.

Query meeting recordings with LlamaIndex MCP Server

Fetch meeting transcripts using `get_transcript` and index them for rapid retrieval. When you ask your agent about a past conversation, it pulls the exact timestamped details using `get_recording`. This setup turns passive audio files into active data assets. Your agent can reference real discussions to answer complex project questions.

Keep action items synchronized and indexed

Use `list_action_items` to monitor outstanding tasks across your team. LlamaIndex indexes these tasks alongside your project files so you can check if meeting decisions match your codebase status. When a task is done, the agent can call `complete_action_item` via the MCP Server. Make sure to review these updates to ensure your indexed task data remains highly accurate.

Setup guide

Set up Fellow MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Fellow MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Fellow tools.",
)
response = await agent.run("List recent Fellow data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Fellow. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Fellow MCP in LlamaIndex

Yes. Use `get_transcript` to retrieve the text, then index it into your vector store. Your agent can then search past conversations semantically.

Set up a pipeline that checks the metadata from `list_notes` before indexing. You can filter out old or archived items to keep your vector index fresh.

Yes. You can call tools like `get_action_item` and `get_note` asynchronously using `to_tool_list_async` to speed up your RAG queries.

Use the allowed_tools filter when setting up McpToolSpec. This lets you restrict the agent to read-only tools like `list_notes` if you want to avoid accidental edits.

Your meeting notes and action items are fetched over an encrypted HTTPS connection. The MCP Server ensures your raw meeting data is passed directly to your local LlamaIndex vector store without persistent cloud storage.

Use it with your favorite AI tools

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