Enables LLM clients to explore and query a local SQLite database through read-only, typed tools for table listing, schema inspection, ad-hoc SQL, customer lookup, and sales summaries.

Native local development environment for Windows, macOS & Linux. A modern alternative to XAMPP, MAMP, Laragon and Laravel Herd, with runtimes, databases, web servers, local sites, HTTPS, AI coding tools and MCP.

A fast object-first data engine for applications and AI agents.

Local-first Apple Health MCP server. Lets AI assistants answer questions about your sleep, workouts, activity and heart data from a local export.

A local MCP server that places a safety layer between AI coding agents and Postgres, parsing every statement, holding risky or large writes for human approval, and journaling all activity on your machine. It enables one-command undo of individual agent-made changes and blocks destructive operations outright.

Enables small local models and other agents to read and query spreadsheet files locally, run SQL and cleaning, audit formulas, make Excel-correct edits, and generate branded dashboards with token-budgeted responses across selectable tool tiers.

MCP server for PostgreSQL: local, Docker, RDS, Neon, Supabase, or behind an SSH bastion.

DataNexa is a local, read-only database MCP gateway for AI agents. It routes SQLite, MySQL, PostgreSQL, and any JDBC-compatible database through a single localhost endpoint, enforces read-only access via SQL AST validation with row, time, and connection limits, and keeps a local audit trail of every query.

Enables AI assistants to read and write a local-first SQLite memory vault from inside a chat — orienting on inherited context, searching and fetching past decisions and notes, wandering linked memories, and recording new ones on demand. All data stays on the user's machine, shared across local and cloud tools via MCP.

Provides AI coding tools with one local SQLite memory and rules store they can read over MCP, REST or the CLI, so facts, sessions and goals persist across sessions and shared rules are written into each tool's own instruction file. Runs entirely on your machine with no account, telemetry or network calls by default.

Exposes local recommendation tools over MCP so clients can rank groceries with explainable scoring across nutrition, price, promotions and close-to-expiry offers. Also supports configurable offer monitoring, weekly plan digests and deduplicated notifications backed by SQLite state.

A local Python + SQLite MCP memory service that stores persona, shared experiences, facts, agreements, and unfinished work, then restores them within a context budget so a new model can continue the same conversation. It supports offline original-text persistence, Chinese keyword retrieval, provenance-tracked memories, persona versioning/rollback, and progressive fading of older events.

Enables AI agents to look up Chinese laws and statutory articles fully offline from a local SQLite database, resolving a law name plus article number to verbatim text, searching articles by keyword, and retrieving a law's chapter structure and version history. It also verifies citations in bulk, checking whether each quoted provision exists word-for-word and is currently in force.

Enables MCP clients to curate YouTube watch history and locally search selected transcripts using SQLite FTS5, ChromaDB embeddings, and reciprocal rank fusion, with tools to browse unindexed history and add videos.

Enables an AI assistant to query Plaid-synced bank data stored locally in SQLite — balances, transactions, spending, cash flow, recurring charges, and liabilities — plus bounded read-only SQL and local CSV/JSON exports, all over local stdio with no hosted service or listening port.

Enables local creation and querying of projects, storage and retrieval of source text, reading and saving storyboard tables with revision-conflict protection, and Excel export through MCP tools backed by SQLite.

Enables LLMs to query a local GnuCash MariaDB database through read-only MCP tools for account balances, recent transactions, spending and income by category, transaction search, and net worth without risking data mutation.

Enables AI agents to store decisions as a versioned history, linking each new decision to the one it replaces so that recall always resolves to the decision currently in force instead of obsolete ones. It runs fully locally on a single SQLite file with BM25 search, and can optionally detect replacements automatically via an LLM or heuristic linker.

Enables Claude Code and Codex to write, update, and retrieve durable long-term memories that survive session boundaries and context compaction. All recall runs locally against a single SQLite file using hybrid BM25, embedding, and recency ranking cut to an explicit token budget, with no cloud dependency.

Enables triaging incoming messages by classifying them into a small bounded set of action categories with a fast model, then routing each one to dismiss noise, send notifications for action items, or optionally auto-register calendar events. Users bring their own source, notifier, and calendar through small adapter interfaces, with verdicts and state kept in local SQLite storage.