"author:0rkz/byte-mcp-server" matching MCP servers:

Enables AI agents to run BigQuery queries safely under a configurable policy, with parsing, dry-run byte limits, row caps, and audit logging for every request.

Enables AI agents to manage plain .jsonl files as tables, offering tools to list libraries and tables, query data, and upsert rows while preserving byte-for-byte fidelity on unchanged rows.

Enables exploring Google BigQuery metadata and running bounded, read-only GoogleSQL queries with dry-run validation, byte limits, and paged results.

Enables AI tools to query a PostgreSQL database, read its schema, list and describe tables, and explain queries while enforcing read-only transactions, row and byte limits, and timeouts.

Enables agents to run SELECT-only queries and inspect table metadata against configured databases via MCP Proxy, with timeouts, row and byte caps, and audit logging.

Captures checkpoints of project files and SQLite tables before an AI agent makes risky changes, then reports differences and rolls them back byte-for-byte, including dropped tables and agent-created junk. Enables agents to snapshot, diff, list, and restore working state with explicit confirmation required before any destructive action.

Enables read-only access to Google BigQuery, allowing users to list tables, inspect metadata, and run SELECT queries with enforced row and byte limits.

An MCP server for managing a book catalog, enabling listing, searching by title, author, or genre, adding books, and evaluating arithmetic expressions.

Lets AI agents estimate the cost and result size of BigQuery and Snowflake queries before they run, then execute them only within per-call byte, row, and dollar bounds. Estimates are labeled with an accuracy tier so agents never over-trust an approximate figure.

Enables AI agents to query Trino and Apache Pinot lakehouses under enforced governance, where every SQL statement is AST-validated, table-allowlisted, priced from the engine's own plan before it runs, and blocked or admitted against scan-byte and intermediate-row budgets. It also grounds agents with schema discovery tools, returns verified results with warnings instead of misleading answers, and records every tool call on an audit trail.

This MCP server gives AI assistants over business data a provenance layer: every number in an answer traces back to specific records, auditable and tamper-evident. It enforces that the model never generates figures—only selects operations whose results are computed deterministically in SQL.

Enables safe read-only access to allowlisted Google Cloud BigQuery tables with schema inspection, keyword search, cost estimation, and strict FinOps guardrails. It supports cross-project billing and limits queries by byte caps and row limits.

A provably read-only SQL layer for AI agents (MCP) and weekly reports — over the database behind an ERP (Logo Tiger, Netsis, Mikro). Statement-checking guard, measured against 28 attacks.

Enables MCP clients such as Claude, ChatGPT and Cursor to answer business questions about sales, customers, orders and spreadsheets in natural language by reading live Postgres/SQLite databases and Google Sheets without exporting files or gaining write access. Guardrails include AST-based read-only SQL validation, read-only database roles, row/byte caps with pagination, PII masking, bearer-token auth for remote HTTP use, and a full JSONL audit log of every tool call.

Enables inspecting SQLite database schemas, executing read-only SELECT queries, and monitoring query performance through MCP tools.

Enables AI agents to discover a configured FHIR R4 endpoint, search and page within a single trusted patient, read exact resources, and inspect retained results as FHIR-shaped JSON with provenance, coverage signals, and byte limits.

Enables Claude to search and analyze Korean academic papers using the Korea Citation Index (KCI) Open API. Supports paper search, detailed metadata retrieval, reference analysis, author and keyword searches, and citation index queries.