Where do I obtain my Readwise Access Token?

You can quickly find or generate your specific access token by logging into your Readwise account and directly visiting `https://readwise.io/access_token`. Copy the alphanumeric token presented there and insert it directly into the prompt required by this integration.

Can the AI add native highlights or upload PDFs to Reader for me?

Currently, the integration server functions on a Read-Only standard format designed specifically to fetch existing knowledge. It fetches metadata or full-text values from highlights, tags, and Reader items, but it doesn't support publishing or updating new sources back to Readwise natively.

Does it also search through to my saved Readwise Reader feed?

Yes. Tools like `list_reader_documents` and `get_reader_document` are designated explicitly for extracting articles or stored reads directly residing inside your connected Readwise Reader account, keeping both typical highlights and Reader data separate but reachable.

Readwise MCP Server for LangChain 6 tools — connect in under 2 minutes

Name: Readwise
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Readwise through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "readwise": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Readwise, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* 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.

LangChain's ecosystem of 500+ components combines seamlessly with Readwise through native MCP adapters. Connect 6 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Readwise MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 6 tools from Readwise via MCP

Why Use LangChain with the Readwise MCP Server

LangChain provides unique advantages when paired with Readwise through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine Readwise MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Readwise queries for multi-turn workflows

Readwise + LangChain Use Cases

Practical scenarios where LangChain combined with the Readwise MCP Server delivers measurable value.

RAG with live data: combine Readwise tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Readwise, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Readwise tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Readwise tool call, measure latency, and optimize your agent's performance

Readwise MCP Tools for LangChain (6)

These 6 tools become available when you connect Readwise to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

Common issues when connecting Readwise to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Readwise + LangChain FAQ

Common questions about integrating Readwise MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.