Flomo MCP Server

The mcp-server-flomo server bridges the Model Context Protocol ecosystem with Flomo, a popular micro‑note service. It resolves a common pain point for developers and AI practitioners: the need to persist concise thoughts, reminders, or research snippets directly from an AI assistant into a lightweight personal knowledge base. By exposing a single, well‑defined tool——the server lets any MCP‑compatible client (such as Claude Desktop) append text to a Flomo inbox with minimal friction.

What the server does

When an AI assistant invokes , the MCP server forwards the supplied content to Flomo’s incoming‑webhook endpoint. The request is authenticated via a user‑supplied , which embeds the personal webhook key. The server handles HTTP communication, error handling, and JSON formatting, so the AI client only needs to supply the note text. This eliminates boilerplate networking code from application logic and keeps the interaction declarative.

Why it matters for developers

Developers building AI workflows often need to capture fleeting insights without leaving the assistant’s context. Flomo’s API is intentionally simple—just a POST to a URL—but integrating it manually can be tedious and error‑prone. The MCP server abstracts this pattern into a reusable tool, allowing developers to:

• Maintain clean AI prompts: The assistant can simply call without embedding API secrets or request details.
• Centralize configuration: The webhook URL is stored once in the client’s config, making it easy to rotate keys or switch environments.
• Leverage existing Flomo features: Notes written via the server appear in the same inbox as those from the web or mobile app, enabling tagging, sharing, and search downstream.

Key features in plain language

• Single‑tool simplicity: Only one tool, , keeps the server lightweight.
• TypeScript foundation: The implementation is written in TypeScript, providing type safety and easier maintenance for contributors.
• Standard MCP integration: The server follows the MCP spec, making it plug‑and‑play with any compliant client.
• Environment‑based configuration: The webhook URL is supplied through an environment variable (), which the client passes via its config file.
• Debugging support: A built‑in inspector script exposes a browser UI for inspecting requests and responses, easing troubleshooting.

Real‑world use cases

• Research assistants: Capture quick observations or URLs while the AI processes a document, then retrieve them later from Flomo.
• Meeting minutes: After summarizing discussions, the assistant writes concise action items directly into Flomo for personal follow‑up.
• Idea management: Store brainstorming sparks as micro‑notes, automatically tagging them for later retrieval.
• Workflow automation: Combine the MCP server with other tools (e.g., calendar or task managers) to create a seamless note‑to‑action pipeline.

Integration with AI workflows

In practice, an MCP client configures the server once. During a session, the assistant can call in response to user prompts or internal triggers. The server’s response is trivial (often just an acknowledgment), so the conversation remains focused on higher‑level tasks. Because the note is persisted immediately, developers can rely on Flomo as a durable, searchable memory layer that complements the stateless nature of most AI models.

Standout advantages

• Zero‑code integration: No custom HTTP client code is needed in the AI application.
• Security by design: The webhook key never leaves the client’s environment; it is passed to the server at runtime.
• Extensibility: The TypeScript codebase invites contributors to add more Flomo features (e.g., tagging, fetching) without breaking existing tools.
• Community tooling: The included MCP Inspector aligns with the broader MCP ecosystem, ensuring consistent debugging experiences across servers.

Overall, mcp-server-flomo delivers a focused, reliable bridge between AI assistants and Flomo, turning fleeting thoughts into structured, searchable notes with minimal developer effort.