get_similar_content_by_ragLABSCO SUMMARY
The reports are on your disk, not behind an API.
That is the trade: a heavier install — Git LFS, a local embedding model, a first-run embedding pass the README puts at roughly 20 minutes — in exchange for search that costs nothing per query and sends nothing anywhere. Budget the setup time, and remember to run git lfs pull; without it the clone succeeds and the library is empty.
What it is
A self-contained knowledge base of consulting and industry reports, packaged as an MCP server. The reports ship with the repository and are stored locally; retrieval works both by keyword and by vector similarity against a bundled embedding model, so nothing has to leave the machine to answer a question.
What you get
- A bundled corpus — the shipped statistics record 174 files across 9,320 pages, 9 publishers including McKinsey, PwC, BCG, Bain, Deloitte and Accenture, and 93 topics
get_similar_content_by_ragfinds the closest passages by vector similarity, using the built-inQwen3-Embedding-0.6Bmodel- Keyword retrieval over report details for when you know the term you want
- Pagination on results, and the local path of each referenced file so you can open the source
- Your own PDFs join the same library: drop them in
library_files, runmain.py, and they are extracted, parsed and optionally embedded - Report pages can be browsed online from inside the MCP client
Requirements
Python 3.12 or newer, plus Git and Git LFS — the corpus comes down through git lfs pull, so a plain clone leaves you with an empty library. Published as InsightsLibrary version 0.1.0. Setup is uv venv .venv and uv sync inside the clone, then register uv with --directory pointing at the project root, followed by run and ikb_mcp_server.py. Nothing is needed for the bundled reports. Adding your own PDFs is the only part that needs credentials: set VLM_API_KEY, VLM_BASE_URL and VLM_MODEL_NAME in .env for the vision model that reads them.
Setup effort
Build from source — git clone https://github.com/v587d/InsightsLibrary.git
USE CASES
01
Finding what the big firms have published on a theme
Search by meaning rather than exact wording and get the closest passages with their source files.
02
Chasing an exact term across the library
Use keyword retrieval when you know the phrase and want every place it appears.
03
Adding your own research to the same index
Drop PDFs into the library folder and run the extraction pass so they are searchable alongside the bundled reports.
04
Reading the page a claim came from
Follow the local file path returned with each result back to the original report.
1 tool
Listed in project docs. Your client may show a different list.
BEFORE YOU INSTALL
What to know first
Tools you get
1 tool named in the project's documentation.
Where it runs
The documented client runs the server on your machine.
INSTALLATION
Connect your client
Server setup
TERMINAL
git clone https://github.com/v587d/InsightsLibrary.git
cd InsightsLibrary
git lfs pull
uv venv .venv
uv syncMCP Clients
Cline
CLINE · JSON
{
"mcpServers": {
"ikb-mcp-server": {
"command": "uv",
"args": [
"--directory",
"<YOUR_PROJECT_ROOT_DIRECTORY>",
"run",
"ikb_mcp_server.py"
]
}
}
}
