What is Mcp Sqlite

mcp-sqlite is an MCP server designed for SQLite files, enabling AI agents to access structured data without direct access to external systems. It supports Datasette-compatible metadata for human users.

Use cases

Use cases for mcp-sqlite include providing structured data access for AI applications, enabling data exploration and analysis without exposing the underlying database, and facilitating the integration of SQLite databases with AI-driven tools.

How to use

To use mcp-sqlite, install the required dependencies, download a sample SQLite database, and create a metadata file that describes the database structure and queries. AI agents can then execute SQL commands and retrieve catalog information using specific commands.

Key features

Key features include the ability for AI agents to obtain the structure of all tables and columns with a single command, enrich metadata with descriptions, and execute arbitrary SQL queries. Additionally, canned queries can be defined in the metadata file for easy access.

Where to use

mcp-sqlite can be used in various fields such as data analysis, machine learning, and application development where structured data from SQLite databases is needed, especially when AI agents are involved.

mcp-sqlite

Provide useful data to AI agents without giving them access to external systems. Compatible with Datasette for human users!

Features

• AI agents can get the structure of all tables and columns in the SQLite database in one command - sqlite_get_catalog.
  • The catalog can be enriched with descriptions for the tables and columns using a simple YAML or JSON metadata file.
• The same metadata file can contain canned queries to the AI to use.
  Each canned query will be turned into a separate MCP tool sqlite_execute_main_{tool name}.
• AI agents can execute arbitrary SQL queries with sqlite_execute.

Quickstart

1. Install uv.
2. Download the sample SQLite database titanic.db.
3. Create a metadata file titanic.yml for your dataset:

   databases:
     titanic:
       tables:
         Observation:
           description: Main table connecting passenger attributes to observed outcomes.
           columns:
             survived: "0/1 indicator whether the passenger survived."
             age: The passenger's age at the time of the crash.
             # Other columns are not documented but are still visible to the AI agent
       queries:
         survivors_of_age:
           title: Count survivors of a specific age
           description: Returns the total counts of passengers and survivors, both for all ages and for a specific provided age.
           sql: |-
             select
               count(*) as total_passengers,
               sum(survived) as survived_passengers,
               sum(case when age = :age then 1 else 0 end) as total_specific_age,
               sum(case when age = :age and survived = 1 then 1 else 0 end) as survived_specific_age
             from Observation
   

4. Create an entry in your MCP client for your database and metadata

   {
     "mcpServers": {
       "sqlite": {
         "command": "uvx",
         "args": [
           "mcp-sqlite",
           "/absolute/path/to/titanic.db",
           "--metadata",
           "/absolute/path/to/titanic.yml"
         ]
       }
     }
   }
   

Your AI agent should now be able to use mcp-sqlite tools sqlite_get_catalog, sqlite_execute, and sqlite_execute_main_survivors_of_age!

Interactive exploration with MCP Inspector and Datasette

The same database and metadata files can be used to explore the data interactively with MCP Inspector and Datasette.

MCP Inspector	Datasette

MCP Inspector

Use the MCP Inspector dashboard to interact with the SQLite database the same way that an AI agent would:

1. Install npm.
2. Run:

   npx @modelcontextprotocol/inspector uvx mcp-sqlite path/to/titanic.db --metadata path/to/titanic.yml
   

Datasette

Since mcp-sqlite metadata is compatible with the Datasette metadata file, you can also explore your data with Datasette:

uvx datasette serve path/to/titanic.db --metadata path/to/titanic.yml

Compatibility with Datasette allows both AI agents and humans to easily explore the same local data!

MCP Tools provided by mcp-sqlite

• sqlite_get_catalog(): Tool the agent can call to get the complete catalog of the databases, tables, and columns in the data, combined with metadata from the metadata file.
  In an earlier iteration of mcp-sqlite, this was a resource instead of a tool, but resources are not as widely supported, so it got turned into a tool.
  If you have a usecase for the catalog as a resource, open an issue and we’ll bring it back!
• sqlite_execute(sql): Tool the agent can call to execute arbitrary SQL. The table results are returned as HTML.
  For more information about why HTML is the best format for LLMs to process, see Siu et al.
• sqlite_execute_main_{canned query name}({canned query args}): A tool is created for each canned query in the metadata, allowing the agent to run predefined queries without writing any SQL.

Usage

Command-line options

usage: mcp-sqlite [-h] [-m METADATA] [-w] [-v] sqlite_file

CLI command to start an MCP server for interacting with SQLite data.

positional arguments:
  sqlite_file           Path to SQLite file to serve the MCP server for.

options:
  -h, --help            show this help message and exit
  -m METADATA, --metadata METADATA
                        Path to Datasette-compatible metadata YAML or JSON file.
  -w, --write           Set this flag to allow the AI agent to write to the database. By default the database is opened in read-only mode.
  -v, --verbose         Be verbose. Include once for INFO output, twice for DEBUG output.

Metadata

Hidden tables

Hiding a table with hidden: true will hide it from the catalog returned by the MCP tool sqlite_get_catalog().
However, note that the table will still be accessible by the AI agent!
Never rely on hiding a table from the catalog as a security feature.

Canned queries

Canned queries are each turned into a separate callable MCP tool by mcp-sqlite.

For example, a query named my_canned_query will become a tool sqlite_execute_main_my_canned_query.

The canned queries functionality is still in active development with more features planned for development soon: