Insights Knowledge Base(IKB) MCP Server

>>>中文版

> 🍭A free, plug-and-play knowledge base. Built-in with 10,000+ high-quality insights 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

1. 🍾 Zero configuration required, designed for *plug-and-play* usage.

2. 🚀 Built-in `Qwen3-Embedding-0.6B` embedding model, related reports can be retrieved through vector search.📢 Report details can also be searched via keyword retrieval.

3. 🍥 over 100 insights reports from well-known consulting firms such as McKinsey, PwC, and BAIN have been collected, including 6,000+ report pages, covering 70+ topics.

4. 💎 *Real-time* online browsing of full reports in MCP Client.

5. 🎉 *Ultra-fast* response: All Function_call returns typically 💡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(Confirm successfully installed Git and Git LFS)

git clone https://github.com/v587d/InsightsLibrary.git
cd InsightsLibrary
git lfs pull

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 install .  # 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.Cline

> Note: Replace `` with actual root directory.

{
  "mcpServers": {
    "ikb-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "", 
        "run",
        "ikb_mcp_server.py"
      ]
    }
  }
}

• Cherry Studio

--directory

run
ikb_mcp_server.py

Adding Private Documents to ikb_mcp_server

1. Configure VLM models and parameters in `.env`:

VLM_API_KEY=
    VLM_BASE_URL= # https://openrouter.ai/api/v1
    VLM_MODEL_NAME= # qwen/qwen2.5-vl-72b-instruct:free

2. Upload the PDF document to the `library_files` folder under the project root directory.

3. Manually run main.py.

# Navigate to the project root directory
# Activate the virtual environment
uv run main.py
(InsightsLibrary) PS D:\Projects\mcp\InsightsLibrary> uv run main.py
[INFO] extractor: PDF extraction initialized | Files directory: library_files | Pages directory: library_pages
[INFO] extractor: Starting scan of directory: library_files
[INFO] extractor: Found 69 PDF files
[INFO] extractor: Scan completed | Total files: 69 | Processed: 0 | Failed: 0
[INFO] recognizer: No pages to process.
# Data has been updated to the database
============================================================
Confirm if you need to create text vector embeddings
⚠️ This process may take approximately 20 minutes
============================================================
Create embeddings? (Enter Y or N): 
# Y: create text vector embeddings
# N: Skip text vector embeddings and exit program

License

This project is licensed under the MIT License. See the LICENSE file for details.

Optimization Updates as of June 17th

1. 💡Optimized `models.py`: Improved data query efficiency by 1,000%

2. 💡Optimized `extractor.py`: Slightly enhanced PDF extraction efficiency

3. 💡Optimized `recognizer.py`: Boosted image comprehension efficiency by 50%

4. 💡Optimized `ikb_mcp_server.py`:

5. 💡Add MIT License(https://github.com/v587d/InsightsLibrary/pull/1#issuecomment-2969226661)

6. 📦 Overall compressed project package size reduced by approximately 50%

7. 💡Streamline Private Document Handling

8. 💡Fixed other identified bugs

Optimizations as of June 22

1. Added `embedder.py`: Implements text vectorization indexing via local Qwen3-Embedding-0.6B model, stored in faiss_index.

2. Modified `main.py`: Closed-loop workflow *PDFExtractor → IMGRecognizer → Embedder (optional)*.

3. New `@mcp.tool(): get_similar_content_by_rag`: Finds most similar document content via vector similarity (RAG).

4. All admin-uploaded reports now support online viewing → Removed library_files folder to reduce project size.

5. Added 2000+ report pages.