Readwise MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Readwise 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 Readwise. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Readwise?"
)
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 Readwise MCP Server
Connect your Readwise account directly to your AI agent. Enabling this integration turns your AI into an expert research assistant, capable of instantly scanning your entire timeline of book highlights, article snippets, tweet saves, and personal tags directly from your unified Readwise and Readwise Reader library.
LlamaIndex agents combine Readwise tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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
- Highlight Retrieval — Perform searches or bulk retrievals of every snippet, quote, or highlight you've ever saved from your Kindle, Apple Books, and web browsers.
- Library Browsing — Ask your AI to list all the books, articles, and sources currently populated in your Readwise database.
- Readwise Reader Documents — Full access to list and extract content directly from articles and feeds saved into your Readwise Reader app.
- Tag Management Analysis — Retrieve the categorizations and tags you use to organize your knowledge base system.
The Readwise MCP Server exposes 6 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 Readwise to LlamaIndex via MCP
Follow these steps to integrate the Readwise 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 6 tools from Readwise
Why Use LlamaIndex with the Readwise MCP Server
LlamaIndex provides unique advantages when paired with Readwise through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Readwise tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Readwise tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Readwise, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Readwise tools were called, what data was returned, and how it influenced the final answer
Readwise + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Readwise MCP Server delivers measurable value.
Hybrid search: combine Readwise real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Readwise 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 Readwise for fresh data
Analytical workflows: chain Readwise queries with LlamaIndex's data connectors to build multi-source analytical reports
Readwise MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Readwise to LlamaIndex via MCP:
check_auth_status
Verifies the validity of the Readwise access token
get_reader_document
Retrieves details for a specific Reader document
list_books
Lists all books and sources in Readwise
list_highlights
Lists all highlights from the user's Readwise account
list_reader_documents
Lists documents in the Readwise Reader
list_tags
Lists all tags used in Readwise
Example Prompts for Readwise in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Readwise immediately.
"List the most recent 5 books I highlighted on Readwise."
"Show me the text of the recent document I saved to Reader with the ID 1234."
"Search my highlights for any mentions of 'productivity'."
Troubleshooting Readwise MCP Server with LlamaIndex
Common issues when connecting Readwise to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpReadwise + LlamaIndex FAQ
Common questions about integrating Readwise 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 Readwise 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 Readwise to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.