Sequel

Sequel positions itself as the data layer for AI agents — an MCP (Model Context Protocol) server that brokers secure access between an agent and an organization's cloud data sources. Rather than embedding credentials in every agent configuration or building bespoke connectors for each database, teams connect a source once in Sequel and let any MCP-compatible agent query it through a unified interface.

The architecture sits behind a single HTTPS endpoint (https://api.sequel.sh/mcp) and exposes five tools to the agent: sequel_manage_connections for listing and adding sources, sequel_search for discovering available data and fetching an execution plan, sequel_execute for running one-off queries, sequel_workbench for interactive exploration, and sequel_download_file for retrieving output artifacts. A session-based workflow ties these together: an agent calls sequel_search to get a plan and a session_id, then threads that session identifier into subsequent execute and workbench calls so they share the same working context and file outputs.

Source coverage spans cloud databases (RDS, Supabase, Neon), data warehouses (BigQuery, Snowflake, ClickHouse), product analytics (Amplitude, Google Analytics), and SaaS tools (Ahrefs). Each source connection stores its own credentials in Sequel — the agent never sees them. Sequel authenticates the agent itself through two methods: a static API key (Bearer token, keys prefixed sql_...) for CLI tools and code editors like Claude Code, Cursor, VS Code, and Windsurf, or OAuth 2.0 with PKCE for connected-platform clients that support it. Both methods scope access to whatever data sources the organization has connected, and nothing beyond that.

Setup is designed for speed: a single curl command installs the Sequel CLI, and sequel login creates an API key and writes the MCP configuration automatically. For teams that prefer manual configuration, the docs provide copy-paste-ready JSON snippets. Once connected, agents can query across sources in natural language — asking about backlinks in Ahrefs, retention funnels in Amplitude, or row counts in a production PostgreSQL instance — without switching tools or managing separate SDKs.

The trade-off is clear: Sequel adds an intermediary layer, so query latency and reliability inherit the characteristics of the upstream APIs it wraps. The Ahrefs integration, for instance, notes that API access requires an eligible paid plan and that reports consume API units billed against the user's Ahrefs account. Similarly, Amplitude connectivity depends on project-level API and secret keys with correct region selection. Teams evaluating Sequel should weigh the convenience of unified agent access against the operational dependency on both Sequel's MCP server and each connected source's API availability.

For organizations already invested in the MCP ecosystem, Sequel fills a genuine gap between agent capabilities and data access. It is less a database client and more an access-control and abstraction layer — one that could simplify how teams bring their own data into AI Data Mining workflows without scattering credentials across agent configurations.