"MCP server that supports JAR decompilation" matching MCP servers:

Enables interaction with MongoDB databases through CRUD operations, aggregation, and schema discovery, with automatic field validation and ObjectId conversion.

An MCP server that provides targeted PostgreSQL schema access to AI agents, retrieving only relevant tables for a question and enforcing read-only access through multiple layers.

An MCP server that provides a USDA-accurate food database with deterministic macro math, enabling tools like list_foods, get_food, calculate_macros, filter_by_diet, build_meal, and list_available_tags. It allows Claude to look up nutrition facts and solve for exact macro targets offline, without AI or network.

SchemaBrain is an MCP server that sits between AI agents (Claude, Cursor, Windsurf) and your SQL database as a read-only trust + intelligence layer. The agent never writes SQL — 12 tools compile it from definitions you control, PII and secret categories are refused before the query runs, and every call lands in a tamper-evident (SHA-256-chained) audit log. Postgres today.

A Model Context Protocol server that exposes Microsoft Edge browsing history across multiple profiles, allowing listing profiles and fetching history for a given day.

A read-only MCP server for ServiceNow that provides deep visibility across multiple instances via query, schema inspection, identity, workflow, and code search tools.

Zero-config MCP server that empowers AI agents to safely query SQL and NoSQL databases like PostgreSQL, MySQL, SQLite, MongoDB, and Redis.

A read-only MCP server that reports BI pipeline readiness, blockers, and the next allowed action for governed Power BI workflows. It never writes files, executes warehouse work, or grants human approvals.

A Model Context Protocol server that gives AI agents real-time access to Excel workbooks in OneDrive, answering natural-language questions with live data. It caches only sheet structure, never cell values, and fetches current ranges via Microsoft Graph for each query.

An MCP server that gives models semantic context about a data warehouse, including table meaning, units, lineage, and health, to reduce guessing.

MCP server that exposes tools for natural-language querying of the Chinook SQLite database, enabling agents to discover schema and execute read-only SQL queries dynamically.

A cooperative guardrail for AI agents that blocks destructive shell commands, SQL statements, or cloud operations until a human approves them out-of-band, with a tamper-evident local ledger.

Read-only MCP server that exposes your local Notion backups to AI agents — query databases, read pages, search a backed-up workspace. Offline by default: no LLM calls, no telemetry, no cloud.

An MCP server that gives AI agents access to configured databases (PostgreSQL, MySQL, Redshift, SQL Server) with SSH/AWS SSM tunnels, pluggable secret providers, and strict per-instance isolation.

MCP server that lets clients run SQL (select/insert/update/delete) against a MySQL database, with readonly and readwrite modes.

A secure PostgreSQL Model Context Protocol server that provides read-only database access through tools like schema listing, object inspection, query execution, and EXPLAIN analysis, with database-enforced access control via read-only transactions and a minimal role rather than SQL text parsing.

An MCP server that provides safe, read-only access to a PostgreSQL warehouse, enabling schema introspection, guarded SQL queries, and truncated result reporting for LLMs.

Read-only Postgres access with a policy gate that blocks writes and restricts visible tables and columns.

An MCP server that enables MCP-compatible AI tools to read-only query Azure Database for PostgreSQL Flexible Server using your existing Azure CLI login, with auto-discovery, schema inspection, and arbitrary read-only SQL. No passwords or connection strings are stored; authentication uses short-lived Entra tokens.

A unified Model Context Protocol server for data engineering and analytics that lets LLM clients query, profile, transform, and visualize data using DuckDB and PySpark, while keeping raw data out of the model's context through read-only, bounded, and audited operations.