DataNexusProxy: Instantly Expose Data Stores via API or MCP Endpoint

🚀 Live Demonstration Sandbox available at: https://centralmind.ai

Introducing DataNexusProxy

This utility offers an effortless mechanism to present your underlying structured data repositories to sophisticated Artificial Intelligence agents, utilizing either the Model Context Protocol (MCP) or the OpenAPI 3.1 specification.

bash docker run --platform linux/amd64 -p 9090:9090 \ ghcr.io/centralmind/gateway:v0.2.18 start \ --connection-string "postgres://db-user:db-password@db-host/db-name?sslmode=require"

This command initiates an accessible interface:

shell INFO Gateway server initialized successfully! INFO MCP Server for Agent Streaming (SSE) is operational at: http://localhost:9090/sse INFO RESTful API with interactive Swagger documentation is reachable at: http://localhost:9090/

This API surface is immediately consumable within your intelligent agents:

Gateway dynamically crafts AI-optimized endpoints.

Rationale for DataNexusProxy

LLM-driven applications necessitate rapid, secure access to enterprise information. We have engineered an abstraction layer that automatically furnishes secure, efficiency-tuned APIs for your structured data assets. - Swift initiation via MCP, OpenAPI, or direct/raw SQL interfaces. - Automatic sanitization of Personally Identifiable Information (PII) and sensitive records to maintain adherence with GDPR, CPRA, SOC 2, and analogous regulatory mandates. - Integration of comprehensive auditing and traceability features, demystifying AI application behavior and allowing security oversight. - Optimized specifically for AI interactions: features native Model Context Protocol (MCP) support with enriched metadata for agent comprehension, complemented by integrated caching and security mechanisms.

This tool is invaluable when an AI assistant requires the capability to construct, modify, or interrogate database records during development cycles. Furthermore, it democratizes analytical access, enabling natural language querying against your data warehouse or transactional systems. Enhance your AI entities by providing access to database information through remote function invocation/tool invocation capabilities.

Core Capabilities

• ⚡ Autonomous API Provisioning – Leverages LLMs to fabricate service interfaces based on table schemas and data sampling.
• 🗄️ Relational Data Compatibility – Supports numerous backends including PostgreSQL, MySQL, ClickHouse, Snowflake, MSSQL, BigQuery, Oracle Database, SQLite, ElasticSearch
• 🌍 Multi-Protocol Delivery – Exposes services via traditional REST or as an MCP Server (including SSE stream mode).
• 🔐 Authorization Mechanisms – Native integration for API Credential Verification and OAuth flows.
• 🔒 Data Leakage Prevention – Deploys regex-based filters or the Microsoft Presidio module for automatic PII and sensitive attribute masking.
• 👀 Observability Suite – Connects with OpenTelemetry (OTel) for granular request tracing and compliance logging.
• 📦 Deployment Flexibility – Accommodates self-hosted LLM deployments through configurable endpoints.
• 🤖 Diverse AI Model Interoperability - Supports communication with OpenAI, Anthropic, Amazon Bedrock, Google Gemini & Google VertexAI
• ⚡ Configuration Agility – Extensible via declarative YAML structures and a modular plugin architecture.
• 📜 Interface Definition – Automatically generates comprehensive Swagger documentation and adheres to the OpenAPI 3.1.0 standard.
• 🔑 Data Access Constraints – Enables granular data governance via custom Lua scripting for Row-Level Security (RLS).
• 🏎️ Performance Tuning – Incorporates temporal and Least Recently Used (LRU) caching methodologies.

Operational Workflow

1. Connection & Schema Elicitation

DataNexusProxy establishes a link to your structured repository (e.g., PostgreSQL) and autonomously analyzes the data structure and representative subsets. The optimized API blueprint is then synthesized based on the initial user prompt. LLMs are exclusively engaged during this initial configuration phase, maintaining a security boundary. The system leverages configured AI Providers for generating the API map while enforcing security policies through PII scanning.

2. Deployment Strategies

Deployment options range from a self-contained executable, Docker containerization, to orchestration via Kubernetes clusters. Consult our startup manual for step-by-step setup. Configuration relies on YAML definitions and pluggable modules for straightforward customization.

3. Consumption & System Integration

Access your data streams through standard REST calls or the specialized Model Context Protocol (MCP), benefiting from inherent security safeguards. DataNexusProxy integrates fluidly with major AI ecosystems such as LangChain, OpenAI, and Claude Desktop via tool invocation or the MCP interface used by Cursor. Telemetry streams can be forwarded to local or remote OTel endpoints for centralized monitoring.

Technical Documentation

Initiation Sequence

• Expedited Setup Guide
• Installation Protocols
• API Blueprint Creation Tutorial
• Service Launch Procedures

Supplementary References

• Integration Guide for ChatGPT
• Database Adapter Specifications
• Plugin System Documentation

Compilation Instructions

shell

Obtain source repository

git clone https://github.com/centralmind/gateway.git

Navigate into project root

cd gateway

Fetch required modules

go mod download

Compile the executable

go build .

Blueprint Generation Process

DataNexusProxy utilizes sophisticated LLMs to construct the operational API manifest. Follow these stages:

Select an authorized AI processing engine: - OpenAI and all compatible endpoints - Anthropic - Amazon Bedrock - Google Vertex AI (Anthropic) - Google Gemini

Google Gemini offers a significant complimentary usage tier.

Obtain an API key via Google AI Studio:

• Google AI Studio Key Portal

Upon logging in, generate your credential within the API access section. The complimentary tier provides sufficient monthly capacity for development and initial testing.

Configure authorization for the chosen engine. For Gemini, export the key:

bash export GEMINI_API_KEY='yourkey'

1. Initiate the discovery mechanism:

bash ./gateway discover \ --ai-provider gemini \ --connection-string "postgresql://neondb_owner:MY_PASSWORD@MY_HOST.neon.tech/neondb?sslmode=require" \ --prompt "Assemble a secure, read-only API facade"

1. Monitor the generation sequence:

shell INFO 🚀 API Schema Ingestion Phase Commenced INFO Stage 1: Configuration Loading INFO ✅ Stage 1 complete. Finished.

INFO Stage 2: Repository Introspection INFO Discovered Datasets: INFO - payment_dim: 3 fields, 39 records INFO - fact_table: 9 fields, 1000000 records INFO ✅ Stage 2 complete. Finished.

Further operational logs...

INFO ✅ All operational stages concluded.

INFO --- Performance Summary --- INFO Total execution duration: 1m10s INFO Token consumption: 16543 (Estimated outlay: $0.0616) INFO Repositories analyzed: 6 INFO Service methods generated: 18 INFO Sensitive fields identified: 2

1. Examine the resulting manifest in gateway.yaml:

yaml api: name: Secure Readonly Service description: '' version: '1.0' database: type: postgres connection: YOUR_CONNECTION_INFO tables: - name: payment_dim columns: # Schema fields endpoints: - http_method: GET http_path: /some_path mcp_method: some_method summary: A descriptive summary description: 'Detailed explanation' query: SQL Statement with parameters params: # Input definitions

Activating the Service

Local Execution

shell ./gateway start --config gateway.yaml

Container Orchestration (Docker Compose)

shell docker compose -f ./example/simple/docker-compose.yml up

MCP Protocol Interfacing

DataNexusProxy fully supports the MCP standard for streamlined integration with applications like Claude. Refer to our Claude integration guide for setup specifics.

To register this tool with Claude Desktop, modify Claude's configuration file:

{ "mcpServers": { "dataNexus": { "command": "PATH_TO_GATEWAY_BINARY", "args": ["start", "--config", "PATH_TO_GATEWAY_YAML_CONFIG", "mcp-stdio"] } } }

Project Trajectory (Roadmap)

This plan is adaptable and heavily influenced by community input. Current development priorities include:

Data Source & Connectivity Enhancements

• 🗄️ Broader Data Source Support - Integration targeting Databricks, Redshift, S3 (Iceberg/Parquet), Oracle, MS SQL Server, Elasticsearch.
• 🔑 Secure Tunneling - Capability to establish connections via SSH jump hosts or bastions.

Functional Expansion

• 🔍 Advanced Query Constructs - Allowing complex filter specifications and aggregate functions as formal parameters.
• 🔐 Robust Authentication - Implementation of API key schemes and OAuth 2.0 mechanisms.

Platform Refinements

• 📦 Schema Lifecycle Management - Automated handling of API versioning and schema evolution.
• 🚦 Traffic Governance - Introduction of intelligent request throttling and rate limiting controls.
• ✍️ Write Operations Support - Enabling data manipulation via INSERT and UPDATE commands.

Registry Listings for DataNexusProxy

• https://mcpreview.com/mcp-servers/centralmind/gateway
• https://mcp.so/server/gateway/centralmind
• https://smithery.ai/server/@centralmind/gateway
• https://www.pulsemcp.com/servers/centralmind-database-gateway

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