What problem does the Privacy SQL MCP Analyst solve?

Commercial LLMs cannot be safely granted direct raw credentials to production databases due to data privacy regulations and security boundaries. First-generation text-to-SQL tools crash on complex joins, hallucinate nonexistent column names, and fail silently on schema dialects. Non-technical founders and executives wait days for junior BI analysts to run basic ad-hoc cohort and churn queries.

How does the Privacy SQL MCP Analyst work?

Protocol Initialization: The MCP server communicates over standard input/output (stdio) or Server-Sent Events (SSE) using the JSON-RPC 2.0 specification, exposing get_database_schema, validate_query, and execute_query. ReAct Execution Cycle: The LLM inspects the natural question, invokes get_database_schema, reasons over foreign key relationships, drafts ANSI-SQL, and invokes execute_query. AST Validation: sqlglot parses the abstract syntax tree to verify the statement is strictly an analytical SELECT before touching the database engine. Sandboxed Execution: Runs against a read-only PostgreSQL replica under a dedicated role with SET TRANSACTION READ ONLY and 5-second execution timeout. Autonomous Self-Correction: If the database engine returns an error, the ReAct loop catches the raw engine traceback, injects it back into context, and self-corrects the query syntax (capped at 3 iterative attempts). Deterministic UI Mapping: The agent outputs structured data and visualization hints, automatically rendering the appropriate Recharts component in the React frontend.

Which tools and integrations does it use?

It is built on FastMCP · Python 3.12 · PostgreSQL · React and integrates Python 3.12, FastMCP SDK, PostgreSQL, React, Recharts, GPT-4o, sqlglot, Docker.

What results does it deliver?

Eliminated the need for a junior BI analyst for ad-hoc database queries. Achieved secure, zero-data-leakage database querying for non-technical founders. 94% first-run SQL accuracy, jumping to 99.2% after autonomous self-correction. Ad-hoc report turnaround time compressed from 48 hours to under 8 seconds.

Custom MCP Server for a Privacy-First SQL AI Analyst

Custom Python Model Context Protocol (MCP) server with self-correcting ReAct SQL loops, read-only PostgreSQL sandboxing, and zero PII data leakage.