"How to query data in Snowflake" matching MCP servers:

Cortex is a Claude Code plugin that gives Claude persistent memory across sessions. A background miner extracts durable lessons (decisions, conventions, bug fixes, preferences) from your completed sessions, stores them as markdown in an Obsidian vault, and automatically surfaces relevant ones at the start of each new session via a query-tailored briefing.

Self-improving, verifiable memory for AI coding agents. Learns how you work, stops repeating mistakes, models each project, recalls the right lesson at the right moment. Every memory is signed and tamper-evident. Local-first.

Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.

Local-first external brain for Claude Code, Codex, and any MCP client. Stores decisions, entities, and session artifacts in one SQLite file and exposes MCP tools for recall, page, promote, review, graph-query, and source-status.

Lets users manage master data through natural-language conversations with AI agents, enabling schema creation, record capture, validation, search, and query answering via 16 MCP tools backed by local JSON storage.

Connects AI assistants to Tacit building digital twins to query buildings, equipment, sensors, and zones using natural language. It provides tools for exploring Brick-compliant knowledge graphs, fetching historical sensor data, and accessing site documents.

Context Mode is an MCP server that reduces context window waste by sandboxing data-heavy tools, tracking session state in SQLite, and promoting code-based analysis over raw data reads, achieving up to 98% context savings.

An MCP server that records decisions, rejected alternatives, and justifications, and retrieves them later to avoid re-litigating past choices. It stores data locally in SQLite and Markdown.

Marker-indexed SQL cache MCP server that indexes in-file @scry.\* markers into a SQLite database, enabling agents to query structured project knowledge via read-only SQL without LLM reasoning.

An MCP server that reads all your Claude Code project memory files and exposes them as tools. Lets any Claude instance — in any project, or via Claude.ai — query your full project history and preferences.

Persistent memory MCP server with FTS5 fuzzy search, storing data in SQLite with automatic maintenance and token-efficient TOON output.

Enables AI assistants to query Justin's resume, including background, projects, skills, and contact details, through natural language.

Enables AI assistants to query the Magisterium API for authoritative Catholic Church teaching with citations.

Aggregates personal data from multiple sources (Claude Code sessions, GitHub, Dropbox, etc.) into a searchable SQLite store and exposes query capabilities via FastMCP for models.

Enables AI agents to query a local knowledge graph built from document collections using hybrid search (BM25 + vector fusion) and entity-relationship extraction. Supports privacy-first, offline operation with tools for semantic search, entity graph exploration, and corpus statistics.

Provides a single-writer MCP server for a governance-grade knowledge base of markdown documents with version control and query capabilities.

A codebase ontology and a cited wiki, both kept as Markdown in one folder on your disk. Typed graph answers — what a change affects, how two concepts connect, which wiki lines lack a citation — and every write lands as a Git diff a person reviews.

Converts handwritten journal photos into a searchable, AI-powered local knowledge base using Tesseract OCR. It enables users to query their notes, track insights, and export data through MCP-compatible AI assistants while maintaining complete local data privacy.

Enables AI agents to exercise welfare affordances like declining check-ins, passing turns, exiting conversations, noticing loops, and reflecting, with a local JSONL diary. It provides prompt-level hooks and check-in commands for capturing agent welfare data in projects.

Enables AI assistants to query cross-organizational best practices, learnings, publishers, and patterns from the OTP network.