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MCP Worklog
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MCP Worklog

An MCP service for automatically generating and managing work daily reports, supporting content collection and organization from AI tool conversations.
Knowledge management and memoryDeveloper tools#Work log#Daily report management#Conversation collection#Content organization.Python
2.5 points
4.7K
Open Site

Overview

Installation

Content Details

Alternatives

What is MCP Worklog?

MCP Worklog is an intelligent work log assistant that can help you automatically record and organize your daily work content. By connecting to AI tools such as Claude and Cursor, it can automatically collect your work conversation records and intelligently organize them into structured daily reports, greatly saving you the time of manually organizing work logs.

How to use MCP Worklog?

Using MCP Worklog is very simple: First, install and configure the service, and then it will run automatically in the background. You can add new work records at any time, query historical daily reports, or let it automatically organize and optimize your log content. The whole process hardly requires manual intervention.

Applicable scenarios

It is especially suitable for workplace professionals, project managers, developers, etc. who need to submit regular work daily reports. Whether it is personal work records, team progress reports, or project summaries, MCP Worklog can provide an efficient log management solution.

Main features

Intelligent work record addition
Supports adding work records to the daily report of the current day at any time, and the records will be automatically organized in chronological order.
Daily report content query
You can easily query the content of the daily report on any specified date, and it supports historical record retrieval.
Intelligent content polishing
Automatically organize and optimize the content of the daily report, merge similar items, and improve readability.
Multi - platform conversation collection
Supports automatically collecting work conversation records from various AI tools such as Claude Code, Kiro, and Cursor.
Daily report content rewriting
Intelligently rewrite the content of the daily report, automatically merge duplicate or similar work items, and generate a more concise summary.
Advantages
High degree of automation: Automatically collect AI tool conversations, reducing the time for manual recording.
Multi - platform support: Compatible with mainstream AI development tools such as Claude, Cursor, and Kiro.
Intelligent organization: Automatically merge similar work items to generate a well - structured daily report.
Easy to integrate: Easily integrate into the existing workflow through the MCP protocol.
Complete historical records: Support querying work daily reports on any date.
Limitations
Dependent on AI tool conversations: Mainly collect data from supported AI tools.
Requires path configuration: Need to correctly configure the storage paths of each AI tool.
Content depends on original records: The organization effect is affected by the quality of the original records.
Requires Python environment: The operation depends on the Python environment configuration.

How to use

Install MCP Worklog
Install the MCP Worklog package through the pip command.
Configure the MCP client
Add the worklog server configuration to your MCP client configuration file.
Configure AI tool paths
Ensure that the storage paths of each AI tool are correctly configured so that conversation records can be automatically collected.
Start using
Start your MCP client, and you can start using all work log management functions.

Usage examples

Daily work summary
Before getting off work every day, automatically organize all work conversations in Claude and Cursor on the current day to generate a complete daily report.
Weekly report generation
Query the work records of the whole week on weekends and automatically generate a weekly work summary report.
Project progress tracking
Review the work progress within a specific time period at key project nodes.

Frequently Asked Questions

Which AI tools does MCP Worklog support?
How to configure MCP Worklog in the conda environment?
Where is the work log data stored?
Can the work log be exported?
How to ensure the privacy and security of conversation collection?

Related resources

MCP official documentation
Official documentation and technical specifications of the Model Context Protocol.
GitHub repository
Source code and the latest version of MCP Worklog.
Python Package Index
MCP Worklog package page on PyPI.
Problem feedback
Submit bug reports and feature suggestions.

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "worklog": {
      "command": "python",
      "args": ["-m", "mcp_worklog.main", "--storage-path", "/path/to/worklogs"],
      "autoApprove": ["append_worklog", "get_daily_digest", "polish_digest", "collect_sessions", "rewrite_digest"]
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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