Grain MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Grain as an MCP tool provider through 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 Grain. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Grain?"
)
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 Grain MCP Server
Connect your Grain.com account to any AI agent and take full control of your team meeting recordings, automated transcriptions, and AI-powered insights through natural conversation.
LlamaIndex agents combine Grain 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
- Meeting Orchestration — List all meeting recordings in your workspace and retrieve primary entry points for workspace interactions natively
- Live Detail Retrieval — Resolve deep specific objects including transcripts and speaker attribution mapped by recording ID flawlessly
- AI Transcription — Download full text structures with speaker attribution, parsing raw linguistic data to review critical discussions limitlessly
- Contextual Insights — Extract high-level abstract reductions including sentiment mapping, summaries, and key takeaways generated by Grain's ML engines
- Action Item Tracking — Filter targeted follow-up tasks detected automatically within meeting scopes to automate post-call workflows
- Highlight Navigation — Identify curated clips and key moments generated by users within specific timestamps to focus on critical insights
- Global Search — Execute keyword scanning across all meeting recordings to find specific discussions and ranked datasets synchronously
- Asset Ingestion — Ingest remote video streams by passing public URLs for initial structural transformations and AI processing securely
- Team Oversight — Retrieve fully enumerated team maps tracking workspace members and authenticated user profiles natively
The Grain 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.
How to Connect Grain to LlamaIndex via MCP
Follow these steps to integrate the Grain 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 12 tools from Grain
Why Use LlamaIndex with the Grain MCP Server
LlamaIndex provides unique advantages when paired with Grain through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Grain tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Grain tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Grain, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Grain tools were called, what data was returned, and how it influenced the final answer
Grain + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Grain MCP Server delivers measurable value.
Hybrid search: combine Grain real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Grain 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 Grain for fresh data
Analytical workflows: chain Grain queries with LlamaIndex's data connectors to build multi-source analytical reports
Grain MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Grain to LlamaIndex via MCP:
get_action_items
Extract all action items identified from a recording
get_current_user
Retrieve the authenticated Grain user profile
get_insights
Retrieve AI-generated insights from a recording
get_recording
Retrieve full details of a specific meeting recording
get_transcript
Retrieve the full timestamped transcript of a meeting with speaker names
list_highlights
List all highlights (curated clips) from a recording
list_recordings
List all meeting recordings in the Grain workspace
list_shared_clips
List all clips that have been shared from the workspace
list_tags
List all tags used across recordings and highlights
list_workspace_members
List all members of the Grain workspace
search_recordings
Search across all meeting recordings by keyword
upload_video
Upload an external video URL for processing by Grain
Example Prompts for Grain in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Grain immediately.
"List my meeting recordings from today"
"What were the key decisions in the 'Roadmap Sync' meeting?"
"Search for recordings mentioning 'pricing strategy'"
Troubleshooting Grain MCP Server with LlamaIndex
Common issues when connecting Grain to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGrain + LlamaIndex FAQ
Common questions about integrating Grain 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 Grain 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 Grain to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.