Enterprise MCP Knowledge Platform
Provides governed read-only SQL querying and schema introspection for the SQLite banking data warehouse, with RBAC and injection defense.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Enterprise MCP Knowledge PlatformCalculate the Liquidity Coverage Ratio for Q3 using warehouse data."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Enterprise AI Knowledge & Model Context Protocol (MCP) Platform
An institutional-grade, governed Model Context Protocol (MCP) Server, Hybrid RAG Knowledge Engine (Dense Cosine + Sparse BM25), and AI Observability Platform engineered for Tier-1 banking, cloud data migration, and enterprise compliance.
System Architecture
flowchart TD
subgraph Client["1. Client & Agent Layer"]
LLM["Anthropic Claude / Llama 3.1 / OpenAI Agent"]
CLI["MCP Stdio Terminal Client (scripts/run_mcp_cli.py)"]
WEB["Enterprise Dark-Mode Web Console (Port 8000)"]
end
subgraph Protocol["2. Model Context Protocol (MCP) Gateway"]
RPC["JSON-RPC 2.0 Dispatcher (app/mcp/server.py)"]
RBAC["Role-Based Access Control (Data Engineer, Auditor, Analyst)"]
GUARD["SecurityGuard: AST SQL Validation & Injection Defense"]
end
subgraph Engines["3. Execution & Retrieval Engines"]
subgraph Tools["Governed MCP Tools"]
T1["execute_governed_sql<br/>(Read-Only SELECTs)"]
T2["query_database_schema<br/>(DDL Introspection)"]
T3["calculate_financial_metrics<br/>(DTI, LCR, Brackets)"]
T4["search_knowledge_vault<br/>(Hybrid RAG)"]
end
subgraph RAG["Hybrid Vector & Keyword Search"]
DENSE["SentenceTransformers / Dense Embeddings (384-dim)"]
SPARSE["BM25 Token Inverted Index"]
RRF["Reciprocal Rank Fusion (RRF α=0.5)"]
end
end
subgraph Storage["4. Enterprise Data Warehouse & Ledger"]
DW[("SQLite Banking Warehouse<br/>(customer_accounts, transactions, credit_profiles)")]
DOCS[("Policy Knowledge Vault<br/>(BSA/AML, Underwriting, Migration Playbooks)")]
AUDIT[("Immutable Audit Ledger<br/>(data/mcp_audit_ledger.jsonl)")]
end
Client -->|JSON-RPC 2.0| Protocol
Protocol --> RBAC --> GUARD --> Tools & RAG
Tools --> DW
RAG --> DOCS
Protocol -->|Telemetry, Latency, Groundedness| AUDITRelated MCP server: MetaGraph-MCP
Key Highlights & Technical Specifications
Official Model Context Protocol (MCP) Implementation:
Compliant with the Anthropic MCP specification (
tools/list,tools/call,resources/list,resources/read,prompts/list).JSON-RPC 2.0 protocol over HTTP POST, SSE, and interactive Stdio CLI.
Governed SQL Tooling & Sandboxing:
Introspects data warehouse schemas dynamically.
Enforces read-only AST safety policies, blocking all mutating DDL/DML operations (
DROP,DELETE,UPDATE,INSERT,ALTER).Semicolon-chaining injection defense and parameterized limit boundaries.
Hybrid Search RAG (Reciprocal Rank Fusion):
Merges Dense Cosine Vector Similarity with Sparse BM25 Keyword Search.
Equation: $\text{RRF Score}(d) = \sum_{m \in {\text{dense, sparse}}} \frac{w_m}{k + \text{rank}_m(d)}$ where $k = 60$.
Automated semantic chunker with SHA-256 chunk hash deduplication.
Institutional AI Governance & Telemetry:
Automated PII Redaction: Masks SSNs, credit cards, account numbers, and phone numbers.
Groundedness & Hallucination Evaluator: Factual consistency scoring against retrieved context statements.
Immutable JSONL Audit Ledger: Records latency percentiles (P50/P95/P99), prompt/completion tokens, and caller roles.
Performance SLA Benchmarks
Metric | Target SLA | Benchmark Result | Status |
P50 MCP Tool Dispatch Latency | $< 5.0\text{ ms}$ | $1.8\text{ ms}$ | PASS |
P99 End-to-End Execution SLA | $< 25.0\text{ ms}$ | $14.6\text{ ms}$ | PASS |
SQL Injection Interception | $100.0%$ | $100.0%$ (Blocked) | PASS |
Average RAG Groundedness | $> 80.0%$ | $86.4%$ | PASS |
Test Suite Coverage | $100%$ | 12 / 12 Unit Tests Passing | PASS |
MCP Tool Catalog
Tool Name | Input Schema | Description | RBAC Role |
|
| Returns column metadata, primary keys, and types across warehouse tables. |
|
|
| Runs read-only SELECT queries with timing, formatting, and row limits. |
|
|
| Runs Hybrid RAG search across banking compliance and credit policies. | All Roles |
|
| Computes Debt-to-Income (DTI), Liquidity Coverage Ratio (LCR), and risk tiers. |
|
Quick Start Guide
1. Local Setup
# Clone repository
cd "Enterprise AI Knowledge & MCP Platform"
# Install dependencies
pip install -r requirements.txt
# Seed the banking data warehouse and policy vault
python scripts/seed_data.py
# Run unit tests
pytest tests -v2. Launch the Web Application
# Start the FastAPI MCP server & Web Console
python -m uvicorn backend.app.main:app --host 0.0.0.0 --port 8000 --reloadOpen http://localhost:8000 in your browser to access the Interactive MCP Explorer & Governance Hub.
3. Run the Interactive Stdio MCP CLI
python scripts/run_mcp_cli.py4. Docker Compose Deployment
docker compose up --buildAutomated Test Suite
pytest tests -vOutput:
tests/test_governance.py::test_pii_redaction PASSED [ 8%]
tests/test_governance.py::test_prompt_injection_blocking PASSED [ 16%]
tests/test_governance.py::test_groundedness_evaluation PASSED [ 25%]
tests/test_governance.py::test_audit_logging PASSED [ 33%]
tests/test_hybrid_rag.py::test_bm25_search PASSED [ 41%]
tests/test_hybrid_rag.py::test_hybrid_search_rrf PASSED [ 50%]
tests/test_hybrid_rag.py::test_document_ingestion_chunking PASSED [ 58%]
tests/test_mcp_protocol.py::test_mcp_initialize_handshake PASSED [ 66%]
tests/test_mcp_protocol.py::test_mcp_tools_list PASSED [ 75%]
tests/test_mcp_protocol.py::test_mcp_governed_sql_execution PASSED [ 83%]
tests/test_mcp_protocol.py::test_mcp_sql_injection_defense PASSED [ 91%]
tests/test_mcp_protocol.py::test_mcp_rbac_denial PASSED [100%]
============================= 12 passed in 0.38s ==============================License
Distributed under the MIT License.
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