flomo-mcp
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-mcpcreate a note about today's ideas"
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 MCP Server
write notes to flomo
This is a TypeScript-based MCP server that implements a simple notes system. It demonstrates core MCP concepts by providing:
Resources representing text notes with URIs and metadata
Tools for creating new notes
Prompts for generating summaries of notes
Features
Resources
List and access notes via
note://URIsEach note has a title, content and metadata
Plain text mime type for simple content access
Tools
create_note- Create new text notesTakes title and content as required parameters
Stores note in server state
Prompts
summarize_notes- Generate a summary of all stored notesIncludes all note contents as embedded resources
Returns structured prompt for LLM summarization
Related MCP server: flomo-mcp
Development
Install dependencies:
npm installBuild the server:
npm run buildFor development with auto-rebuild:
npm run watchInstallation
To use with Claude Desktop, add the server config:
On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"flomo-mcp": {
"command": "/path/to/flomo-mcp/build/index.js"
}
}
}Debugging
Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:
npm run inspectorThe Inspector will provide a URL to access debugging tools in your browser.
Available Tools
1 toolwrite_noteC
Create a new note
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | Text content of the note |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It reveals that the tool creates a note, but does not mention side effects, whether content is overwritten or appended, permissions required, failure behavior, or what response is returned. For a write operation this is a meaningful transparency gap.
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 concise sentence with no filler or redundant words. It is front-loaded and easy to parse, though it is arguably under-specified rather than meaningfully structured.
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, no output schema, and no explanation of return values or post-conditions, the description is thin. The input is simple and fully documented, so the tool is callable, but an agent does not know what happens after the note is created or what the tool returns.
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 100%, with the 'content' parameter already described as 'Text content of the note.' The description adds no parameter-level meaning, but the schema fully documents the only parameter, so a baseline score of 3 is appropriate.
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 states a specific verb and resource: 'Create a new note.' It is clear and unambiguous about the core action, and with no sibling tools named, there is no differentiation burden. However, it offers little beyond restating the tool name and does not add any context about what a 'note' is or where it is stored.
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, what prerequisites exist, or when an alternative might be preferable. Since there are no sibling tools, there is no routing context, but the lack of any usage context still leaves the agent to infer appropriateness entirely from the tool name and description.
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
v0.1.0- First observed
write_note
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion between tools. The single tool has a clear, unique purpose.
The tool name 'write_note' follows a consistent verb_noun pattern, which is clear and predictable even as a standalone.
A single tool for a Flomo integration feels insufficient for the apparent scope of a note-taking service. Most such servers would include at least retrieval or management operations, making this too few.
The tool surface is severely limited, providing only note creation with no ability to read, update, delete, or list notes. This leaves obvious gaps that would prevent agents from performing common workflows.
Maintenance
Related MCP Connectors
Connect AI to your flomo notes. Search, create, edit notes and manage tags via MCP.
MCP-native notes and memory for ChatGPT, Claude, and other AI tools.
- TaprootOAuthcom.taproothq
Persistent memory layer for AI tools. Save and recall notes across Claude and other MCP clients.
Save notes in seconds. Your AI can then search, read, write and tag them over MCP.
Related MCP Servers
- -licenseNot gradedqualityNot gradedmaintenanceA simple notes system that allows creating, storing, and accessing text notes through MCP resources and tools, with built-in prompt support for generating summaries of stored notes.-
- FlicenseBqualityDmaintenanceEnables writing and managing notes in flomo through MCP tools, resources, and prompts.115 npm-
- FlicenseAqualityCmaintenanceEnables an LLM to keep a persistent scratchpad by adding and reading notes, retrieving the latest note as context, and generating summaries through MCP.21-
- AlicenseNot gradedqualityCmaintenanceEnables MCP-compatible clients to create, list, search, and delete notes, read all notes as markdown, and use prompts to summarize or clean up notes.MIT