Query, modify and analyse local SQLite databases in conversation — the fastest way to chat with a data file.
SQLite is everywhere — mobile app data exports, analytics dumps, browser profiles, that .db file a colleague sent you with no documentation — and the SQLite MCP server is the quickest way to have a conversation with one. Point it at a file and your assistant can list tables, inspect schemas, run queries and, unlike its Postgres sibling, actually write: create tables, insert rows, update records.
That write access changes what it's for. Beyond answering questions about existing data, it works as a lightweight data workbench: import a CSV's worth of rows, normalise a messy column, build a summary table, then export findings. For prototyping a schema before committing to a migration, sketching it out in SQLite conversationally is genuinely faster than writing DDL by hand.
The server is Python-based (run it with uvx, no permanent install needed) and includes a small memo resource for accumulating analysis notes during a session — a nice touch when you're several queries deep into exploring a dataset and want the assistant to keep a running summary.
Two things to keep in mind. First, write access means the model can mangle your data — work on a copy of any file you care about. Second, SQLite locks at the file level, so don't point it at a database another application currently has open for writing (looking at you, browser history files). Within those bounds, it's the most frictionless database tool in the MCP ecosystem.
--db-path cheerfully creates an empty database when the file doesn't exist, so a typo in the path gives you a working server with zero tables rather than an error. Run list_tables first when a database looks suspiciously empty.INTEGER can happily store text. When totals come back wrong or sorting looks alphabetical, that's almost always the cause.write_query executes one statement at a time with no transaction wrapping the sequence, so a multi-step cleanup can stop halfway and leave the file in a partial state.append_insight memo lives in the session rather than the database, so ask for a written summary before closing the conversation if the findings matter.App exports and data dumps become explorable in seconds — schema first, then whatever questions follow.
Clean, reshape and summarise tabular data with SQL instead of a spreadsheet.
Sketch and iterate table designs conversationally before writing real migrations.
| Tool | What it does |
|---|---|
| read_query | Run a SELECT query and return rows |
| write_query | Run INSERT, UPDATE or DELETE statements |
| create_table | Create a new table from a CREATE TABLE statement |
| list_tables | List all tables in the database |
| describe_table | Show a table's columns and types |
| append_insight | Add a finding to the running analysis memo |
{
"mcpServers": {
"sqlite": {
"command": "uvx",
"args": ["mcp-server-sqlite", "--db-path", "/path/to/your/database.db"]
}
}
}Requires uv (https://docs.astral.sh/uv). Work on a copy of important files — this server can write.
Python 3.10+ with uv installed (uvx runs the server without a permanent install).
Read-only SQL access to Postgres — let your assistant inspect schemas and answer questions from real data.
Manage your whole Supabase project in conversation — database, auth, storage, Edge Functions and branches.
Metabase ships its own MCP endpoint — search your BI content, build and run queries, and save questions and dashboards without leaving the chat.
Official MongoDB server covering data, schemas and Atlas management — from find queries to spinning up clusters.
Serverless Postgres with database branching — point your assistant at Neon and let it work on disposable copies.
Official Redis server — search keys, inspect data structures and manage caches in natural language.