reg-docs-mcp

An MCP (Model Context Protocol) server that answers questions over insurance and regulatory documents using retrieval-augmented generation (RAG). Any MCP-compatible AI client (Claude Desktop, Claude Code, Cursor) can call it as a tool to get grounded, cited answers instead of relying on the model's memory.

Everything in this stack is free and runs locally — no AWS account, no API keys, no per-call cost.

How it works

1. Regulatory documents (plain text) are chunked into overlapping passages.

2. Each chunk is embedded locally with a small sentence-transformer model.

3. Chunks and their embeddings are indexed into OpenSearch as knn_vector fields.

4. The MCP server exposes a search_docs tool: given a natural-language query, it embeds the query the same way, runs a k-NN similarity search, and returns the top matching passages with their source file and score.

5. An AI client calling the tool gets real, citable text back — not a hallucinated summary.

Related MCP server: RAG-MCP

Prerequisites

• Python 3.10+

• Docker (for local OpenSearch)

Setup

python3 -m venv .venv
source .venv/bin/activate      # Windows: .venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env

docker compose up -d           # starts OpenSearch + OpenSearch Dashboards
python3 ingest.py              # chunks, embeds, and indexes the sample docs
python3 mcp_server.py          # runs the MCP server on stdio

The first ingest.py run downloads the embedding model from Hugging Face (~90 MB) and caches it locally — after that, everything runs offline.

Using it from an AI client

Add this to your MCP client config (e.g. Claude Desktop's claude_desktop_config.json), using absolute paths:

{
  "mcpServers": {
    "reg-docs": {
      "command": "/absolute/path/to/reg-docs-mcp/.venv/bin/python",
      "args": ["/absolute/path/to/reg-docs-mcp/mcp_server.py"]
    }
  }
}

Then ask the client something like "What's the difference between the SCR and the MCR under Solvency II?" and it will call search_docs and answer from the retrieved passages.

Inspecting the index

OpenSearch Dashboards is available at http://localhost:5601 once the containers are up. Under Dev Tools, you can query the index directly to confirm ingestion worked:

GET reg-docs/_search
{
  "query": { "match_all": {} },
  "size": 3
}

Adding real documents

data/sample_docs/ ships with a few short, original placeholder summaries (written for this project, not copied from any official source) so the pipeline works out of the box. For a fuller, more realistic demo, add plain-text extracts from public regulatory sources, for example:

• FCA Handbook — https://www.handbook.fca.org.uk

• Bank of England / PRA Rulebook — https://www.prarulebook.co.uk

• EIOPA (Solvency II) — https://www.eiopa.europa.eu

• IFRS Foundation — https://www.ifrs.org

Drop .txt files into data/sample_docs/ and re-run python3 ingest.py.

Project structure

reg-docs-mcp/
├── requirements.txt
├── docker-compose.yml       OpenSearch + OpenSearch Dashboards, local only
├── .env.example
├── config.py                 environment/config loading
├── chunk.py                  paragraph/sentence-aware text chunking
├── embeddings.py              local embedding model wrapper
├── opensearch_client.py        index creation, bulk indexing, k-NN search
├── ingest.py                    ingestion pipeline entry point
├── mcp_server.py                 MCP server exposing the search_docs tool
└── data/
    └── sample_docs/               sample text documents

All modules sit flat in the project root rather than inside a package — MCP clients launch mcp_server.py directly as a script, and package- relative imports don't resolve in that context.

Notes

• The OpenSearch containers disable the security plugin for local development convenience. Do not use this configuration for anything exposed beyond localhost.

• all-MiniLM-L6-v2 produces 384-dimensional embeddings; if you swap in a different embedding model, update EMBEDDING_DIMS in config.py to match.

License

MIT