Memo Meister MCP Server for LlamaIndexGive LlamaIndex instant access to 11 tools to Add Memo Comment, Create Memo, Delete Memo, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Memo Meister 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 Memo Meister app connector for LlamaIndex is a standout in the Document Management category — giving your AI agent 11 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 Memo Meister. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in Memo Meister?"
)
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 Memo Meister MCP Server
Connect your Memo Meister account to any AI agent and manage field documentation and project files through natural conversation.
LlamaIndex agents combine Memo Meister tool responses with indexed documents for comprehensive, grounded answers. Connect 11 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
- Project Management — Create and manage project folders and workspaces
- Memo Management — Create, read, and organize documentation memos
- File Handling — Access images, PDFs, and field reports attached to memos
- Task Tracking — Monitor task statuses and comments within projects
- Search — Query projects and memos using GraphQL
The Memo Meister 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.
All 11 Memo Meister tools available for LlamaIndex
When LlamaIndex connects to Memo Meister through Vinkius, your AI agent gets direct access to every tool listed below — spanning field-documentation, smart-notes, construction-reports, 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.
Add a comment to a memo
Create a new memo in a project
Delete a memo
Get information about the current authenticated user
Get details of a specific memo
Get details of a specific project
List comments on a memo
List files attached to a memo
List memos (notes) in a project
List all projects (memosets) in MemoMeister
Update an existing memo
Connect Memo Meister to LlamaIndex via MCP
Follow these steps to wire Memo Meister 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 Memo Meister MCP Server
LlamaIndex provides unique advantages when paired with Memo Meister through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Memo Meister tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Memo Meister tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Memo Meister, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Memo Meister tools were called, what data was returned, and how it influenced the final answer
Memo Meister + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Memo Meister MCP Server delivers measurable value.
Hybrid search: combine Memo Meister real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Memo Meister 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 Memo Meister for fresh data
Analytical workflows: chain Memo Meister queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Memo Meister in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Memo Meister immediately.
"Show recent memos in the 'Downtown Office Build' project."
"Create a new memo for the daily site report."
"List all open tasks assigned to me across projects."
Troubleshooting Memo Meister MCP Server with LlamaIndex
Common issues when connecting Memo Meister to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMemo Meister + LlamaIndex FAQ
Common questions about integrating Memo Meister MCP Server with LlamaIndex.