Notion MCP Server

The Notion MCP server bridges large‑language models (LLMs) with the Notion API, allowing an AI assistant to read and manipulate content inside a user’s workspace. By exposing Notion’s rich set of endpoints as MCP tools, the server eliminates the need for developers to write custom integration code or handle authentication flows. Instead, a single MCP configuration unlocks the full breadth of Notion’s capabilities—querying databases, retrieving pages and blocks, searching, and even listing users—directly from the assistant’s prompt. This capability is especially valuable for developers building knowledge‑management or productivity workflows, as it lets AI agents act as a natural language front‑end to Notion’s structured data.

A standout feature of this server is its Markdown conversion mode. When the environment variable is set to , responses are transformed from raw JSON into Markdown text. This dramatically reduces the token footprint for read‑only operations, making conversations with large models faster and cheaper. While the conversion is experimental and can interfere with edit operations (since the original JSON structure is lost), developers can toggle the output format on a per‑request basis using the parameter, ensuring they receive JSON when modifications are required and Markdown when only viewing.

Key capabilities include:

• Comprehensive tool set: Retrieve pages, blocks, databases, search results, users, and comments; query databases with filters and sorts; list all workspace users.
• Fine‑grained control: The flag lets developers expose only the subset of tools needed for a particular workflow, reducing surface area and potential security risks.
• Token‑efficient interactions: Markdown conversion shrinks payloads, which is critical for cost‑effective LLM usage when dealing with large Notion pages or database queries.
• Seamless integration: The server is launched via a simple command line or npm script and communicates over the MCP protocol, making it drop‑in for any AI assistant that supports MCP.

Typical use cases include:

• Automated knowledge extraction: An AI agent can pull relevant pages from a project database and summarize them for stakeholders.
• Dynamic content generation: The assistant can create or update Notion pages in response to natural‑language prompts, enabling conversational content creation.
• Workflow automation: By querying database views and responding with Markdown summaries, the assistant can drive task boards or status reports without manual copy‑paste.
• User management: Listing workspace users and retrieving profile information can power role‑based access or onboarding assistants.

In practice, a developer adds the server to their MCP configuration and supplies the Notion integration token. Once running, the assistant can invoke tools such as or , receive results in JSON or Markdown, and embed those outputs directly into its responses. This tight coupling between AI reasoning and Notion data streamlines the creation of intelligent productivity applications, turning Notion into a fully programmable knowledge base that responds to natural language queries.