Flomo MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Flomo MCP Serversave this idea about AI agents to my flomo"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Flomo MCP Server
https://github.com/xianminx/mcp-server-flomo
A Model Context Protocol (MCP) server that lets you create notes in Flomo directly through AI chat interactions in Cursor or Claude desktop. Write and organize your thoughts seamlessly through natural language commands.
Usage
Installing via Smithery
To install mcp-server-flomo for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install mcp-server-flomo --client claudemcp-get
npx @michaellatman/mcp-get@latest install mcp-server-flomomcp.so
https://mcp.so/protocol/mcp-server-flomo/wss://mcp.smithery.ai:443
Manually configure
Configure Claude / Cursor / Windsurf / Cline / ChatWise / Cherry Studio etc.
FLOMO_API_URL=your_api_url_here npx mcp-server-flomoOr configure for your MCP Host / Client. See https://mcp.so/protocol/mcp-server-flomo/wss://mcp.smithery.ai:443 for configuration detail.
Get your Flomo API URL from Flomo API Settings
{
"mcpServers": {
"mcp-server-flomo": {
"command": "npx",
"args": [
"mcp-server-flomo"
],
"env": {
"FLOMO_API_URL": "your_api_url_here"
}
}
}
}Once the server is running, you can create notes in Flomo through natural language commands in your AI chat. Here are some examples:
"How to publish a mcp server? Could you save the answer to flomo note after answering it"This will save the Cursor AI Agent response to Flomo directly in the chat conversation!
The server will handle:
Converting your natural language requests into Flomo API calls
Proper formatting and submission of your notes
Providing feedback on successful note creation
Related MCP server: flomo-mcp
Dev
Get your Flomo API URL from Flomo API Settings
Install and configure:
# Install dependencies npm install FLOMO_API_URL=your_api_url_here npx .
Technical Details
Built with TypeScript and the Model Context Protocol SDK
Communicates with Flomo via their REST API
Runs over stdio for seamless integration with AI tools
License
This project is licensed under the MIT License—see the LICENSE file for details.
Available Tools
1 toolnewNoteC
Create a new note in Flomo
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool creates a note but doesn't describe what happens after creation (e.g., success/failure response, permissions needed, rate limits, or whether it's idempotent). This leaves significant gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's appropriately sized for a simple tool and front-loads the essential action and target system.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't address behavioral aspects, parameter meaning, or expected outcomes, leaving the agent with insufficient context to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no information about the single 'input' parameter. It doesn't explain what the input represents (e.g., note content, title, formatting), acceptable formats, or constraints, failing to compensate for the schema's lack of documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create') and resource ('a new note in Flomo'), providing a specific verb+resource combination. It doesn't need to distinguish from siblings since none exist, making the purpose sufficiently clear for standalone use.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, prerequisites, or contextual constraints. It simply states what the tool does without any usage instructions or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- First observed
newNote
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'newNote' has a single, clear purpose: creating a new note in Flomo, so an agent cannot misselect between non-existent alternatives.
The single tool name 'newNote' follows a clear verb_noun pattern (new + Note), and with only one tool, consistency is inherently perfect. There are no other tools to compare against, so no inconsistencies can arise.
A single tool is too few for a note-taking server like Flomo, which typically involves operations beyond just creation, such as retrieving, updating, deleting, or listing notes. This minimal set severely limits agent functionality and feels incomplete for the domain.
The tool surface is severely incomplete for a note-taking service. It only supports creating notes (newNote), with no tools for reading, updating, deleting, searching, or managing notes. This creates significant gaps that will cause agent failures in common workflows.
Maintenance
Related MCP Connectors
Connect AI to your flomo notes. Search, create, edit notes and manage tags via MCP.
- JotterOAuthfyi.jotter
Instantly save notes and conversation handoffs in one AI chat, seamlessly load them in any other.
Securely search, create, and organize your Mem notes and collections from AI assistants.
- TaprootOAuthcom.taproothq
Persistent memory layer for AI tools. Save and recall notes across Claude and other MCP clients.
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- FlicenseBqualityCmaintenanceEnables writing and managing notes to flomo via MCP. Provides tools for creating notes, resources for accessing them, and prompts for summarization.110 npm-
- AlicenseBqualityDmaintenanceEnables AI assistants to interact with Memos instances, supporting multi-instance connections, note management, tags, attachments, and shortcuts.165 npm2MIT
- FlicenseBqualityDmaintenanceEnables writing and managing notes in flomo through MCP tools, resources, and prompts.110 npm-