SEBI Compliance System

A deterministic regulatory compliance evaluation system built for SEBI (Research Analyst) Regulations, 2014.

This project exposes regulatory compliance rules as Model Context Protocol (MCP) tools served by a FastMCP (streamable-http) server. The rules are queried deterministically from a structured JSON file (data/rules.json) without any database, RAG pipeline, or vector store. A LangGraph React Agent powered by Groq (ChatGroq) acts as the MCP client, orchestrating tool calls based on natural language queries and returning verifiable, deterministic audit trails.

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🏗️ Architecture & Core Principles

+-------------------------------------------------------------------------------+
|                        Streamlit Frontend (Port 8501)                         |
|   - Chat Interface & Expandable Deterministic Tool Audit Trails               |
|   - Preset Scenario Buttons for One-Click Live Demos                          |
+-------------------------------------------------------------------------------+
                                       |
                             POST /chat (JSON)
                                       v
+-------------------------------------------------------------------------------+
|                       FastAPI Backend (Port 8001)                             |
|   - MultiServerMCPClient (langchain-mcp-adapters)                             |
|   - LangGraph React Agent (langgraph.prebuilt.create_react_agent)             |
|   - Groq LLM (ChatGroq) orchestration & tool selection logic                  |
+-------------------------------------------------------------------------------+
                                       |
                   MCP Streamable HTTP Transport (Port 8000)
                                       v
+-------------------------------------------------------------------------------+
|                      FastMCP Server (`mcp_server/server.py`)                  |
|   - Tools: check_compliance, get_applicable_rules, get_audit_log              |
|   - Deterministic evaluation using safe operator map (NO eval())              |
+-------------------------------------------------------------------------------+
                                 /            \
                  Reads Rules   /              \  Writes Audit Log
                               v                v
          +------------------------+      +---------------------------+
          |    data/rules.json     |      |    audit/audit_log.json   |
          |  (14 Verified Rules)   |      |  (Persistent Log Array)   |
          +------------------------+      +---------------------------+

🎯 Why Deterministic MCP?

Compliance verdicts (pass, fail, needs_review) must come exclusively from deterministic code evaluating structured JSON rules — NEVER from an LLM judging raw text or hallucinating regulatory limits.

• The LLM's only job is:
  1. Deciding which MCP tool(s) to call based on the user's natural-language question (check_compliance, get_applicable_rules, get_audit_log).
  2. Turning the structured tool output into a readable response while citing exact SEBI regulations and displaying the unique audit_id.
• No eval(): Numeric and boolean rule conditions are evaluated strictly via safe operator mappings (operator.ge, operator.le, operator.eq, operator.contains).
• Qualitative Rules: Clauses that require human judgment or documentary verification (e.g. conflict of interest policies or research rationale reasonableness) use operator: "manual_review" and deterministically output needs_review.

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📂 Project Structure

sebi-compliance-mcp/
├── .env.example              # Example environment configuration
├── .env                      # Local environment variables (GROQ_API_KEY)
├── requirements.txt          # Pinned Python package dependencies
├── README.md                 # Project documentation & run guide
├── data/
│   └── rules.json            # 14 hand-authored SEBI Research Analyst rules
├── mcp_server/
│   └── server.py             # FastMCP server exposing compliance evaluation tools
├── backend/
│   └── app.py                # FastAPI backend + LangGraph React Agent + MCP Client
├── frontend/
│   └── streamlit_app.py      # Streamlit interactive chat UI
└── audit/
    └── audit_log.json        # Persistent JSON audit log (generated at runtime)

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🚀 Setup & Installation

1. Prerequisites

• Python 3.11+ installed locally.
• A Groq API Key (GROQ_API_KEY). You can get one for free at console.groq.com.

2. Create Virtual Environment & Install Dependencies

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

3. Configure Environment Variables

Copy .env.example to .env and add your Groq API key:

cp .env.example .env

In .env:

GROQ_API_KEY=your_actual_groq_api_key_here
GROQ_MODEL=llama-3.3-70b-versatile

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▶️ Running the System (Exact Run Order)

[!IMPORTANT] The FastMCP server (server.py) MUST be running BEFORE the FastAPI backend starts, because the backend MultiServerMCPClient connects and fetches available tools from the server during startup.

Open three separate terminal windows/tabs (make sure your virtual environment venv is activated in all three):

Terminal 1: Start FastMCP Server (Port 8000)

python mcp_server/server.py

You should see output indicating the server is running on http://0.0.0.0:8000 (streamable-http).

Terminal 2: Start FastAPI Backend (Port 8001)

uvicorn backend.app:app --reload --port 8001

On startup, the backend connects to http://localhost:8000/mcp, initializes the MultiServerMCPClient, and registers tools with the LangGraph React Agent.

Terminal 3: Start Streamlit Frontend (Port 8501)

streamlit run frontend/streamlit_app.py

Your browser will automatically open at http://localhost:8501 showing the interactive chat UI.

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🛠️ MCP Tools Reference (mcp_server/server.py)

1. check_compliance(entity_type: str, scenario: dict) -> dict

   • Loads rules matching entity_type (individual_RA, partnership_RA, non_individual_RA).
   • Evaluates each scenario attribute against rules.json.
   • Computes overall_status: "fail" if any rule failed, else "needs_review" if any rule requires manual verification or scenario fields are missing, else "pass".
   • Generates a unique audit_id (e.g. q-2026-07-12T15:30:00Z-a1b2c3) and saves the exact execution record to audit/audit_log.json.

2. get_applicable_rules(entity_type: str) -> list

   • Returns all verified SEBI rules and regulatory citations applicable to the specified entity type without evaluating a scenario.

3. get_audit_log(audit_id: str) -> dict

   • Retrieves the persistent log entry from audit/audit_log.json by its unique audit_id.

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📋 Rule Schema (data/rules.json)

Each rule adheres strictly to the following schema:

{
  "rule_id": "SEBI-RA-2014-3.2-a",
  "regulation": "SEBI (Research Analysts) Regulations, 2014",
  "clause_ref": "Regulation 3(2)(a)",
  "entity_type": "individual_RA",
  "condition_type": "net_worth_min",
  "operator": ">=",
  "value": 100000,
  "unit": "INR",
  "effective_date": "2014-09-01",
  "effective_until": null,
  "source_citation": "SEBI (Research Analysts) Regulations, 2014, Reg 3(2)(a)",
  "status": "verified"
}

Covered Conditions & Operators

• Numeric Limits: net_worth_min, experience_years_min, trading_window_lockin_days_before/after, record_keeping_years_min, holding_in_subject_company_max_pct (>=, <=, ==)
• Categorical & Set Matches: qualification_req (in array of allowed qualifications)
• Boolean Requirements: nism_certification_active, compliance_officer_appointed, compensation_from_merchant_banking (==)
• Qualitative Standards (operator: "manual_review"): conflict_of_interest_policy_quality, research_rationale_basis_adequacy (returns needs_review with regulatory targets)

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💡 Example Demo Queries

Try these out in the Streamlit UI or by clicking the preset sidebar buttons:

• Individual RA (Should Fail):

  "Check compliance for an individual_RA with net_worth_min of 80000 INR, qualification_req of post_graduate_degree, nism_certification_active true, experience_years_min of 5, trading_window_lockin_days_before 30, trading_window_lockin_days_after 5, compensation_from_merchant_banking false, holding_in_subject_company_max_pct 0.5, and record_keeping_years_min 5."

• Non-Individual RA (Should Pass / Review):

  "Verify compliance for a non_individual_RA with net_worth_min 3000000, compliance_officer_appointed true, record_keeping_years_min 6, conflict_of_interest_policy_quality documented_and_effectively_enforced, and compensation_from_merchant_banking false."

• Missing Information (Should Trigger Clarification / Needs Review):

  "Check compliance for an individual RA with ₹200,000 net worth."