Readwise MCP Server for LlamaIndexGive LlamaIndex instant access to 16 tools to Check Readwise Status, Create Highlight, Delete Highlight, and more
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.
Ask AI about this App Connector for LlamaIndex
The Readwise app connector for LlamaIndex is a standout in the Productivity category — giving your AI agent 16 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 Readwise. "
"You have 16 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
Transform how your organization interacts with reading material by giving your AI agent full control over your Readwise library. With 16 tools covering full highlight CRUD, book search by source and category, tag management, and daily review access, your agents can retrieve specific passages, create annotations, and help you retain knowledge.
LlamaIndex agents combine Readwise tool responses with indexed documents for comprehensive, grounded answers. Connect 16 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
- Browse books by source or category
- Full CRUD for highlights, notes, and tags
- Access daily spaced repetition reviews
- Export all data incrementally for backup or analysis
Who is it for?
Ideal for researchers, students, and professionals needing instant, conversational access to their curated knowledge base.The Readwise MCP Server exposes 16 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 16 Readwise tools available for LlamaIndex
When LlamaIndex connects to Readwise through Vinkius, your AI agent gets direct access to every tool listed below — spanning reading, spaced-repetition, highlights, 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.
Verify connectivity
Create a highlight
Delete a highlight
Supports incremental export with updatedAfter filter. Export highlights
Get book details
Get daily review
Get highlight details
List all books
List books by category
List books by source
Returns text, note, location, and tags. List highlights
List review queue
List all tags
Search books
Search highlights
Update a highlight
Connect Readwise to LlamaIndex via MCP
Follow these steps to wire Readwise 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 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
Example Prompts for Readwise in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Readwise immediately.
"Find all my highlights related to 'stoicism' and summarize the key themes."
"List all the books I've saved from my Kindle library."
"Create a new highlight for 'The Almanack of Naval Ravikant' with the note: 'Crucial insight on leverage'."
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.