
Insights Knowledge Base MCP Server
A free, plug-and-play knowledge base server that provides access to 10,000+ insight reports with secure local data storage.
Tools
search_report_profile
该方法用于查询多条件组合的报告概况。LLM需根据用户输入的消息(user_message)提炼出以下参数。 ️⚠️注意:当LLM引用该方法返回的结果时,必须用markdown格式明确、醒目告知用户引自哪篇报告和具体访问地址! 比如“**观点引自《Open source technology in the age of AI》** ```Path <如果"file_uri"不为空,这里完整填入file_uri> ```” 参数: keywords: List[str] = None, 整篇报告的关键词。 ⚠️注意: - 将每个关键词自动翻译为中英双语 - 例如用户输入"帮我查询下科技上市公司前景哈?" → 应转换为["科技", "technology", "上市公司", "publicly listed company", "前景", "prospect"] title: str = "", 报告标题包含词。 content: str = "", 报告内容包含词。 publisher: str = "", 报告发布者。 start_date: Optional[datetime] = None, 报告查询开始日期。 end_date: Optional[datetime] = None, 报告查询结束日期。 match_logic: str = "OR", 匹配逻辑。"OR" 或者 "AND",二选一,**优先用 "OR"**。 返回: results:报告概览 - "file_name": 报告名称 - "topic": 报告主题 - "content": 报告整体摘要 - "published_by": 发布机构 - "published_date": 发布日期 - "file_full_path": 报告存放于本地地址 - "matched_keywords": 匹配关键词组 current_page:当前页码。⚠️当前页码小于总页码时,LLM需在结尾处提示用户可输入“下一页”查询更多记录。 total_pages: 总页数 total_matches: 总匹配记录条数
search_content_detail
该方法用于查询符合多条件组合的报告详情页面。LLM需根据用户输入的消息(user_message)提炼出以下参数。 ⚠️注意:当LLM引用该方法返回的结果时,必须用markdown格式明确、醒目告知用户引自哪篇报告和具体访问地址! 比如“**观点引自《21世纪CEO的成功法则》第10、16页** ```Path <如果"file_uri"不为空,这里完整填入file_uri> ```” 参数: keywords: List[str] = None, 报告详情页的关键词。 ⚠️注意: - 将每个关键词自动翻译为中英双语 - 例如用户输入"帮我查询下科技上市公司前景哈?" → 应转换为["科技", "technology", "上市公司", "publicly listed company", "前景", "prospect"] title: str = "", 报告详情页标题包含词。 content: str = "", 报告详情页内容包含词。 publisher: str = "", 报告发布者。 start_date: Optional[datetime] = None, 报告查询开始日期。 end_date: Optional[datetime] = None, 报告查询结束日期。 match_logic: str = "OR", 匹配逻辑。"OR" 或者 "AND",二选一,**优先用 "OR"**。 page_index: int = 1, 页码,默认仅显示第一页。 返回: results:报告详情 - file_name: 详情页来自于��份报告名 - page_number: 页码 - page_abstract: 摘要 - page_content: 完整内容 - page_keywords: 详情页关键词 - published_by: 报告发布机构 - published_date:报告发布日期 - file_full_path: 报告存放于本地地址 - matched_keywords: 匹配关键词组 current_page:当前页码。⚠️当前页码小于总页码时,LLM需在结尾处提示用户可输入“下一页”查询更多记录。 total_pages: 总页数 total_matches: 总匹配记录条数
README
Insights Knowledge Base(IKB) MCP Server
🍭A free, plug-and-play knowledge base. Built-in with 10,000+ high-quality insight reports, packaged as MCP Server, and secure local data storage.
⚠️⚠️ All collected reports in this project come from free resources on official research report websites. ⚠️⚠️
Features
- 🍾 No configuration needed, truly plug-and-play. For private document parsing, configure VLM models and parameters in
.env
(e.g.,VLM_MODEL_NAME=qwen2.5-vl-72b-instruct
). - 🦉 Permanently free - no need to waste effort collecting report resources. Welcome to share reliable, copyright-free report sources via
issues
. - 📢 Committed to weekly report updates, but bug fixes depend on my mood (I'm not a programmer 🤭).
[][https://youtu.be/Mb8KbPo7EVM]
Installation (Beginner-Friendly)
💡Pro tip: Stuck? Drag this page to an LLM client (like DeepSeek) for step-by-step guidance. Actually, these instructions were written by DeepSeek too...
Prerequisites: Python 3.12+ (Download from official website and ADD ENVIRONMENT PATH)
Install UV:
pip install uv
1. Clone the project
git clone https://github.com/v587d/InsightsLibrary.git
cd InsightsLibrary
2. Create virtual environment
uv venv .venv # Create dedicated virtual environment
# Activate environment
# Windows:
.\.venv\Scripts\activate
# Mac/Linux:
source .venv/bin/activate
3. Install core dependencies
uv pip install -e . # Note the trailing dot indicating current directory
4. Create environment variables (for future needs)
notepad .env # Windows
# Or
nano .env # Mac/Linux
5. Configure MCP Server
- VSCODE
Note: Replace
<Your Project Root Directory!!!>
with actual root directory.
{
"mcpServers": {
"ikb-mcp-server": {
"command": "uv",
"args": [
"--directory",
"<Your Project Root Directory!!!>",
"run",
"ikb_mcp_server.py"
]
}
}
}
- Cherry Studio
- Command:
uv
- Arguments:
- Command:
--directory
<Your Project Root Directory!!!>
run
ikb_mcp_server.py
Parse Private Documents
Version 0.1.0 has basic functionality - we'll improve this later. 😎
- Upload PDF documents to the
library_files
folder - Manually run Python scripts:
# cd to project root
# Activate virtual environment
uv run decoder.py
# Wait for completion
uv run large_models.py
# Wait for completion
# Data is now updated in the database
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