Reflect MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Reflect 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 Reflect. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Reflect?"
)
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 Reflect MCP Server
Connect your Reflect account securely to your AI agent via their developer API. This integration grants your AI the ability to directly explore your networked thought graph, lookup personal notes, manage book highlights, and append daily thoughts asynchronously from your conversation interface.
LlamaIndex agents combine Reflect tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Explore Your Graph — Direct your AI to investigate connected insights within your Reflect graphs (
list_graphs). Request lists of your notes (list_notes) or retrieve the specific Markdown content of a single note (get_note). - Capture Ideas Instantly — Ask the agent to establish new permanent notes (
create_note) or quickly dump conversational insights, summaries, and tasks straight into your daily note (append_daily_note). - Analyze Connections — Instruct the AI to map out your thoughts by retrieving all backlinks pointing to a specific subject (
get_backlinks). - Save Links & Books — Let your AI automatically bookmark URLs (
create_link), browse your saved bookmarks (list_links), or explore your imported library of book highlights (list_books).
The Reflect MCP Server exposes 10 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 Reflect to LlamaIndex via MCP
Follow these steps to integrate the Reflect 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 10 tools from Reflect
Why Use LlamaIndex with the Reflect MCP Server
LlamaIndex provides unique advantages when paired with Reflect through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Reflect tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Reflect tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Reflect, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Reflect tools were called, what data was returned, and how it influenced the final answer
Reflect + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Reflect MCP Server delivers measurable value.
Hybrid search: combine Reflect real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Reflect 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 Reflect for fresh data
Analytical workflows: chain Reflect queries with LlamaIndex's data connectors to build multi-source analytical reports
Reflect MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Reflect to LlamaIndex via MCP:
append_daily_note
Optionally specify a list/heading name. Appends Markdown text to today's daily note
create_link
Reflect will automatically attempt to extract metadata. Saves a new web link/bookmark to a Reflect graph
create_note
Specify subject and Markdown content. Creates a new note in a Reflect graph
get_backlinks
Retrieves all notes that link to a specific note
get_current_user
Retrieves profile details for the authenticated Reflect user
get_note
Retrieves the full content and metadata of a Reflect note
list_books
Lists all books saved or imported into Reflect
list_graphs
Lists all Reflect graphs (workspaces) accessible by the user
list_links
Lists all saved links (bookmarks) in a graph
list_notes
Lists all notes within a specific Reflect graph
Example Prompts for Reflect in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Reflect immediately.
"List all available graphs in my Reflect account."
"Create a permanent note titled 'Meeting 2024 Strategy' inside my 'Personal Brain' graph with summary bullet points."
"Find notes linked by backlinks that point to my note 'React Learnings'."
Troubleshooting Reflect MCP Server with LlamaIndex
Common issues when connecting Reflect to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpReflect + LlamaIndex FAQ
Common questions about integrating Reflect 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 Reflect 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 Reflect to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.