MCP 服务器

Insurance Claims MCP RAG Agent

Provides retrieval-augmented generation for insurance claims, enabling search, clause retrieval, and governed tool-calling over policy documents using local LLM (Ollama).

README

MCP-Based Insurance Claims RAG Agent

Local-first enterprise AI engineering project for insurance policy intelligence, semantic retrieval, MCP-style tools, and governed tool-calling workflows.

1. Project Summary

This project implements a local-first insurance claims and policy intelligence agent.

The system ingests insurance and regulatory PDF documents, extracts and cleans page-level text, creates section-aware semantic chunks, generates local embeddings, stores them in a local vector store, retrieves relevant policy context, and uses a local LLM through Ollama to produce grounded answers.

The project also adds an MCP-style policy knowledge server and a governed tool-calling orchestrator. The LLM is not allowed to directly execute backend logic. It proposes a structured tool call, the orchestrator validates the tool name and arguments, the approved tool executes, and the final answer is generated from the tool result.

The project is designed as a local MVP that can later migrate to Claude on Amazon Bedrock and AWS-native retrieval infrastructure.

2. Core Design Principle

The model proposes.
The orchestrator validates.
The registered tool executes.
The retrieval layer provides grounded evidence.
The final answer is generated from retrieved context only.

This is not a simple chatbot. It is a controlled tool-calling system with clear execution boundaries.

3. Key Capabilities

Capability	Description
PDF ingestion	Reads local insurance and regulatory PDF documents page by page
Text cleaning	Normalizes PDF-extracted text and removes extraction noise
Metadata enrichment	Adds document type, provider/regulator, product type, page references, and source category
Section-aware chunking	Preserves insurance document structure instead of using blind token splitting
Local embeddings	Uses Ollama embedding model for local vector generation
Local vector store	Stores normalized embeddings and chunks using a lightweight NumPy-based vector store
Retrieval evaluation	Tests retrieval quality before LLM integration
Local RAG answering	Uses retrieved chunks as controlled context for the LLM
MCP-style tools	Exposes retrieval capabilities as reusable policy tools
Governed tool calling	Forces the LLM to propose structured JSON tool calls that are validated before execution
Streamlit UI	Provides a local chat interface with selected tool, arguments, retrieved sources, and trace output
AWS migration path	Designed to migrate from local Ollama to Claude on Amazon Bedrock

4. System Architecture

flowchart TD
    A[User Question] --> B[Streamlit App or CLI]
    B --> C[Tool Calling Orchestrator]

    C --> D[Local LLM via Ollama]
    D --> E[Structured Tool Call Proposal]

    E --> F[Tool Call Parser]
    F --> G[Schema and Argument Validation]
    G --> H[Tool Registry Allowlist]

    H --> I[MCP Style Policy Tool]
    I --> J[Retrieval Search Service]
    J --> K[Local Embedding Model]
    J --> L[Local NumPy Vector Store]

    L --> M[Policy and Regulatory Chunks]
    M --> N[Source PDF Documents]

    I --> O[Structured Tool Result]
    O --> C
    C --> P[Final Grounded Answer]
    C --> Q[Trace Log]

5. Tool-Calling Lifecycle

sequenceDiagram
    participant User
    participant App as App or CLI
    participant LLM as Local LLM
    participant Orchestrator
    participant Tool as Registered Policy Tool
    participant Retrieval
    participant Store as Vector Store

    User->>App: Ask insurance policy or claim question
    App->>Orchestrator: Send question
    Orchestrator->>LLM: Ask model to select one tool
    LLM-->>Orchestrator: Return JSON tool call
    Orchestrator->>Orchestrator: Parse JSON
    Orchestrator->>Orchestrator: Validate tool name and arguments
    Orchestrator->>Tool: Execute approved tool
    Tool->>Retrieval: Run semantic search
    Retrieval->>Store: Search embeddings
    Store-->>Retrieval: Return top matching chunks
    Retrieval-->>Tool: Return normalized sources
    Tool-->>Orchestrator: Return structured tool result
    Orchestrator->>LLM: Generate final answer from tool result
    LLM-->>App: Grounded answer with source references
    App-->>User: Display answer, sources, and trace

6. Retrieval Pipeline

flowchart TD
    A[Insurance and Regulatory PDFs] --> B[Page Level Text Extraction]
    B --> C[Text Cleaning]
    C --> D[Metadata Enrichment]
    D --> E[Heading and Section Detection]
    E --> F[Section Aware Chunking]
    F --> G[Chunk Type Classification]
    G --> H[Ollama Embedding Generation]
    H --> I[Local NumPy Vector Store]
    I --> J[Semantic Search]
    J --> K[Retrieved Context with Source References]
    K --> L[Grounded LLM Answer]

7. MCP-Style Tool Layer

The project exposes retrieval capabilities as controlled tools.

Tool	Purpose	Risk Level
`search_policy_documents`	Search policy and regulatory document chunks	Low
`get_claim_requirements`	Retrieve evidence, claim conditions, coverage clauses, and exclusions	Low
`get_complaint_obligations`	Retrieve complaint handling and dispute resolution obligations	Low

The LLM does not call arbitrary Python functions. It can only propose calls to registered tools.

The orchestrator checks:

1. Is the tool registered?
2. Are required arguments present?
3. Are argument types valid?
4. Is top_k within allowed limits?
5. Is the tool risk level allowed?
6. Can the tool execute without human approval?

8. Local-to-AWS Migration View

flowchart LR
    A[Local PDF Folder] --> B[Amazon S3 Document Bucket]
    C[Ollama Local LLM] --> D[Claude on Amazon Bedrock]
    E[Ollama Embeddings] --> F[Amazon Bedrock Embeddings]
    G[Local NumPy Vector Store] --> H[Amazon OpenSearch Serverless or Bedrock Knowledge Bases]
    I[Local MCP Style Tools] --> J[AWS Hosted MCP Services]
    K[Local Logs] --> L[Amazon CloudWatch]
    M[Streamlit Local UI] --> N[Cloud Frontend or Internal Enterprise App]

9. Repository Structure

insurance-claims-mcp-rag/
|
|-- app/
|   |-- streamlit_app.py
|
|-- config/
|   |-- retrieval_test_queries.json
|
|-- data/
|   |-- raw/
|   |-- processed/
|   |-- chunks/
|
|-- docs/
|   |-- demo_questions.md
|
|-- logs/
|
|-- scripts/
|   |-- ingest_pdfs.py
|   |-- clean_pages.py
|   |-- chunk_pages.py
|   |-- build_vector_store.py
|   |-- query_vector_store.py
|   |-- evaluate_retrieval.py
|   |-- ask_rag.py
|   |-- test_policy_tools.py
|   |-- run_orchestrator.py
|
|-- src/
|   |-- ingestion/
|   |   |-- pdf_loader.py
|   |   |-- text_cleaner.py
|   |   |-- metadata_enricher.py
|   |
|   |-- chunking/
|   |   |-- section_detector.py
|   |   |-- chunk_builder.py
|   |
|   |-- retrieval/
|   |   |-- embedding_model.py
|   |   |-- vector_store.py
|   |   |-- search_service.py
|   |
|   |-- llm/
|   |   |-- ollama_client.py
|   |   |-- rag_answer_service.py
|   |
|   |-- mcp_server/
|   |   |-- policy_tools.py
|   |   |-- policy_server.py
|   |
|   |-- orchestrator/
|       |-- tool_registry.py
|       |-- tool_call_parser.py
|       |-- tool_orchestrator.py
|
|-- tests/
|
|-- vector_store/
|
|-- .env.example
|-- .gitignore
|-- README.md
|-- requirements.txt

10. Knowledge Base

The local PDF source folder is configured through .env.

Example:

PDF_SOURCE_DIR=C:\Users\SSS\Desktop\AI Project\pdf policy documents

The document set is expected to include insurance and regulatory material such as:

Document Category	Purpose
Product Disclosure Statements	Coverage, exclusions, definitions, and claim conditions
Claims handling guidance	Fair and transparent claims handling context
ASIC regulatory guidance	Claims handling and complaint obligations
General Insurance Code of Practice	Industry conduct and customer treatment standards
APRA CPS 230	Operational risk, resilience, and service provider controls

PDF documents are intentionally not committed to GitHub.

11. Environment Variables

Create a local .env file from .env.example.

PROJECT_NAME=insurance-claims-mcp-rag
PROJECT_ROOT=YOUR_PROJECT_PATH
PDF_SOURCE_DIR=YOUR_POLICY_PDF_FOLDER_PATH

OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_MODEL=qwen2.5:7b
OLLAMA_EMBEDDING_MODEL=nomic-embed-text

VECTOR_DB_DIR=.\vector_store\local
CHUNK_OUTPUT_DIR=.\data\chunks
PROCESSED_OUTPUT_DIR=.\data\processed

12. Local Setup

12.1 Clone the repository

git clone https://github.com/SwapnilMundhekar/insurance-claims-mcp-rag.git
cd insurance-claims-mcp-rag

12.2 Create virtual environment

python -m venv .venv

12.3 Activate virtual environment on Windows

.\.venv\Scripts\Activate.ps1

12.4 Install dependencies

pip install -r requirements.txt

12.5 Pull local Ollama models

ollama pull qwen2.5:7b
ollama pull nomic-embed-text

12.6 Configure `.env`

Copy .env.example to .env and update the local PDF folder path.

13. Build Pipeline

Run the local build in order.

13.1 Ingest PDFs

python -u scripts\ingest_pdfs.py

Expected local output:

data/processed/extracted_pages.json
data/processed/ingestion_summary.json

13.2 Clean pages and enrich metadata

python -u scripts\clean_pages.py

Expected local output:

data/processed/clean_pages.json
data/processed/document_inventory.json

13.3 Build section-aware chunks

python -u scripts\chunk_pages.py

Expected local output:

data/chunks/policy_chunks.json
data/chunks/chunking_summary.json

13.4 Build local vector store

python -u scripts\build_vector_store.py

Expected local output:

vector_store/local/chunks.json
vector_store/local/embeddings.npy
data/processed/vector_store_summary.json

13.5 Query vector store

python -u scripts\query_vector_store.py

13.6 Evaluate retrieval

python -u scripts\evaluate_retrieval.py

Expected local output:

data/processed/retrieval_test_results.json
data/processed/retrieval_test_report.md

13.7 Ask RAG question

python -u scripts\ask_rag.py "Is accidental damage covered under car insurance?"

13.8 Test policy tools

python -u scripts\test_policy_tools.py

13.9 Run governed tool-calling orchestrator

python -u scripts\run_orchestrator.py "What evidence is required for a car insurance claim?"

13.10 Run Streamlit app

python -m streamlit run app\streamlit_app.py

14. Example Questions

Use these questions to test the system.

Is accidental damage covered under car insurance?

What evidence is required for a car insurance claim?

What are complaint handling obligations under ASIC RG 271?

What does the General Insurance Code of Practice say about claims handling?

What does CPS 230 say about operational risk management?

What does CPS 230 say about material service providers?

15. Streamlit App View

The local Streamlit app shows:

Panel	Purpose
Chat	User question and final grounded answer
Selected Tool	Tool chosen by the LLM
Tool Arguments	Validated JSON arguments sent to the tool
Tool Result Count	Number of retrieved chunks
Retrieved Sources	Document names, page ranges, section titles, chunk types, and source references
Raw Trace JSON	Full execution trace for debugging and auditability

Future demo animation location:

docs/assets/demo.gif

Once a real demo GIF is recorded, it can be added with:

![Application Demo](docs/assets/demo.gif)

No fake demo screenshot or fake performance graph is included.

16. Build Roadmap

gantt
    title Local MVP Build Roadmap
    dateFormat  YYYY-MM-DD
    axisFormat  %d %b

    section Document Pipeline
    PDF ingestion                     :done, p1, 2026-06-24, 1d
    Cleaning and metadata             :done, p2, 2026-06-24, 1d
    Section aware chunking            :done, p3, 2026-06-24, 1d

    section Retrieval Layer
    Embeddings and vector store        :done, p4, 2026-06-24, 1d
    Retrieval evaluation              :done, p5, 2026-06-24, 1d

    section Agent Layer
    Local RAG answering               :done, p6, 2026-06-24, 1d
    MCP style policy tools            :done, p7, 2026-06-24, 1d
    Tool calling orchestrator          :done, p8, 2026-06-24, 1d

    section Product Layer
    Streamlit trace app               :done, p9, 2026-06-24, 1d
    AWS migration plan                :active, p10, 2026-06-25, 1d

17. Engineering Decisions

17.1 Why local first?

Local development allows faster iteration, lower cost, and easier debugging before moving to AWS.

17.2 Why Ollama?

Ollama provides a simple local runtime for LLM and embedding models. It avoids cloud cost during the MVP phase.

17.3 Why local NumPy vector store?

The local vector store is lightweight, transparent, and stable for development. It stores normalized embeddings in embeddings.npy and chunks in chunks.json.

For AWS, this can be replaced with OpenSearch Serverless, Bedrock Knowledge Bases, or another managed vector store.

17.4 Why MCP-style tools?

MCP-style tools create a clean boundary between the agent and backend capabilities. Tools have names, descriptions, schemas, and execution ownership.

17.5 Why not let the LLM execute directly?

The LLM should not execute arbitrary functions. It should propose actions. The application should validate and execute only approved tools.

18. Retrieval Evaluation

The project includes a retrieval evaluation suite using predefined test queries.

The evaluation checks:

Metric	Meaning
Top 1 document type match	Whether the first retrieved chunk comes from the expected document category
Top 3 any document type match	Whether any of the top 3 chunks match the expected document category
Chunk type match	Whether the retrieved chunk type is relevant to the question
Source traceability	Whether page and document references are preserved

Generated report:

data/processed/retrieval_test_report.md

This report is local-only and is not committed to GitHub.

19. Tool Trace Example

Each governed tool-calling run saves a trace under:

logs/

A trace includes:

{
  "user_question": "What evidence is required for a car insurance claim?",
  "tool_selection": {
    "tool_call": {
      "tool_name": "get_claim_requirements",
      "arguments": {
        "query": "evidence required for car insurance claim",
        "top_k": 5
      }
    }
  },
  "tool_execution": {
    "tool_name": "get_claim_requirements",
    "risk_level": "low",
    "result": {
      "result_count": 5
    }
  },
  "final_answer": "Based on the retrieved context..."
}

Trace logs are ignored by Git because they are runtime artifacts.

20. Security and Governance Model

The local MVP includes basic governance controls.

Control	Implementation
Tool allowlist	Only registered tools can execute
Schema validation	Tool calls must include valid `query` and `top_k` arguments
Risk gating	Only low-risk tools execute automatically
No arbitrary execution	The LLM cannot call unregistered Python functions
Local secrets	`.env` is excluded from Git
Local runtime artifacts	PDFs, chunks, vector store, and logs are excluded from Git
Traceability	Tool selection, arguments, result, and final answer are saved

Future enterprise controls:

Future Control	AWS Direction
Authentication	IAM, Cognito, or enterprise SSO
Authorization	Role-based tool access
Audit logging	CloudWatch and structured audit tables
Document storage	S3 with bucket policies
Model governance	Amazon Bedrock model access controls
Retrieval governance	OpenSearch metadata filtering
Human approval	Approval gate for side-effecting tools

21. Limitations

This project is a local MVP.

Current limitations:

It uses local synthetic or manually selected documents.
It does not make final legal, regulatory, or claim decisions.
It does not send emails or update claim systems.
It does not yet use a production MCP client session for all tool invocation flows.
It does not include real customer, policyholder, or private claim data.
It does not include cloud deployment yet.
Retrieval quality depends on PDF extraction quality, chunking quality, and embedding relevance.
The local vector store is suitable for development, not enterprise-scale retrieval.

22. Future Enhancements

Planned enhancements:

Enhancement	Description
Production MCP client	Use a full MCP client session to call MCP server tools
Hybrid retrieval	Combine semantic search with keyword search
Reranking	Add a reranker to improve top-k relevance
Better section detection	Improve clause and heading detection for complex PDFs
Evaluation dataset	Add labelled retrieval and answer-quality test cases
Human approval	Add approval workflow for medium/high-risk tools
AWS deployment	Move LLM to Claude on Bedrock
Managed vector DB	Move vector store to OpenSearch Serverless or Bedrock Knowledge Bases
Observability	Add structured logs and latency tracking
Demo GIF	Add real Streamlit walkthrough animation

23. AWS Target Architecture

flowchart TD
    A[User Interface] --> B[Agent API]
    B --> C[Claude on Amazon Bedrock]
    B --> D[Tool Calling Orchestrator]

    D --> E[MCP Policy Knowledge Service]
    D --> F[MCP Claims Service]
    D --> G[MCP Audit Service]

    E --> H[Amazon Bedrock Knowledge Bases or OpenSearch Serverless]
    H --> I[S3 Policy Document Bucket]

    F --> J[Claims System API or Synthetic Claims DB]
    G --> K[CloudWatch Logs]
    G --> L[Audit Store]

    C --> B
    D --> B

24. What This Project Demonstrates

This project demonstrates senior AI engineering concepts:

Area	Demonstrated By
RAG architecture	Document ingestion, chunking, embeddings, retrieval, grounded answers
Tool calling	LLM-generated structured tool proposals
MCP architecture	Tool definitions and server-side policy tools
Orchestration	Validation, allowlist execution, risk control, trace logging
LLMOps thinking	Retrieval evaluation, local-first testing, traceability
Enterprise AI design	Governance, approval boundaries, auditability, migration path
Cloud readiness	Clear path from local Ollama to Claude on AWS Bedrock

25. Author

Swapnil Mundhekar

GitHub: SwapnilMundhekar

26. License

This project is currently for personal learning, portfolio development.

A formal license can be added later.

Insurance Claims MCP RAG Agent

README

MCP-Based Insurance Claims RAG Agent

1. Project Summary

2. Core Design Principle

3. Key Capabilities

4. System Architecture

5. Tool-Calling Lifecycle

6. Retrieval Pipeline

7. MCP-Style Tool Layer

8. Local-to-AWS Migration View

9. Repository Structure

10. Knowledge Base

11. Environment Variables

12. Local Setup

12.1 Clone the repository

12.2 Create virtual environment

12.3 Activate virtual environment on Windows

12.4 Install dependencies

12.5 Pull local Ollama models

12.6 Configure .env

13. Build Pipeline

13.1 Ingest PDFs

13.2 Clean pages and enrich metadata

13.3 Build section-aware chunks

13.4 Build local vector store

13.5 Query vector store

13.6 Evaluate retrieval

13.7 Ask RAG question

13.8 Test policy tools

13.9 Run governed tool-calling orchestrator

13.10 Run Streamlit app

14. Example Questions

15. Streamlit App View

16. Build Roadmap

17. Engineering Decisions

17.1 Why local first?

17.2 Why Ollama?

17.3 Why local NumPy vector store?

17.4 Why MCP-style tools?

17.5 Why not let the LLM execute directly?

18. Retrieval Evaluation

19. Tool Trace Example

20. Security and Governance Model

21. Limitations

22. Future Enhancements

23. AWS Target Architecture

24. What This Project Demonstrates

25. Author

26. License

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12.6 Configure `.env`