DBMCP: Database MCP Server for SQL Server

MCP server that gives AI assistants full read-only access to SQL Server databases -- schema exploration, query execution, and structural analysis. Designed for legacy databases with undeclared foreign keys. All responses use TOON format for minimal token consumption.

Features

• Schema exploration (schemas, tables, columns, indexes, constraints)
• Read-only query execution with CTE support and automatic row limiting
• Query validation via configurable denylist (sqlglot-based)
• Primary key candidate discovery
• Foreign key candidate inference
• Column statistics and analysis
• Azure AD integrated authentication
• TOON-formatted responses (token-efficient for LLM consumers)
• Async database execution with configurable query timeouts

Requirements

• Python 3.11+
• SQL Server (via ODBC Driver 18)
• uv (Python package manager)
• MCP-compatible client (Claude Desktop, Claude Code, etc.)

Installation

1. Global install (recommended for MCP clients)

uv tool install "dbmcp @ git+https://github.com/jesse-smith/dbmcp.git"

2. Local development

git clone https://github.com/jesse-smith/dbmcp.git
cd dbmcp
uv sync

ODBC Driver 18

macOS:

brew tap microsoft/mssql-release https://github.com/Microsoft/homebrew-mssql-release
brew install msodbcsql18

Linux (Ubuntu/Debian):

curl https://packages.microsoft.com/keys/microsoft.asc | sudo apt-key add -
curl https://packages.microsoft.com/config/ubuntu/$(lsb_release -rs)/prod.list | sudo tee /etc/apt/sources.list.d/mssql-release.list
sudo apt-get update
sudo ACCEPT_EULA=Y apt-get install -y msodbcsql18

Windows: Download from: https://learn.microsoft.com/en-us/sql/connect/odbc/download-odbc-driver-for-sql-server

MCP Client Configuration

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "dbmcp": {
      "command": "dbmcp"
    }
  }
}

Claude Code

Add to .mcp.json or configure via CLI:

{
  "mcpServers": {
    "dbmcp": {
      "command": "dbmcp",
      "type": "stdio"
    }
  }
}

If installed locally (not via uv tool), use uv run dbmcp as the command and set cwd to the repo directory.

Configuration

dbmcp loads optional configuration from a TOML file. No config file is required — all settings have sensible defaults.

Config file locations

dbmcp searches for a config file in this order (first match wins):

Priority	Path	Use case
1	./dbmcp.toml	Project-level config, committed to repo or kept local
2	~/.dbmcp/config.toml	User-level config, shared across all projects

Setting up a project-level config

Create dbmcp.toml in the directory where the MCP server runs (usually your project root):

[defaults]
query_timeout = 60          # seconds (5–300, default: 30)
row_limit = 5000            # max rows returned (1–10000, default: 1000)
sample_size = 10            # default sample rows (1–1000, default: 5)
text_truncation_limit = 2000  # chars before truncation (100–10000, default: 1000)

[connections.dev]
server = "localhost"
database = "mydb"
authentication_method = "sql"
username = "sa"
password = "${SA_PASSWORD}"   # resolved from env var at connection time
trust_server_cert = true

[connections.prod]
server = "prod-server.example.com"
database = "proddb"
port = 1434
authentication_method = "windows"

allowed_stored_procedures = ["sp_custom_report", "dbo.my_proc"]

Setting up a user-level config

Create ~/.dbmcp/config.toml for connections and defaults you want available everywhere:

mkdir -p ~/.dbmcp

# ~/.dbmcp/config.toml

[defaults]
query_timeout = 60

[connections.staging]
server = "staging-db.internal"
database = "app_staging"
authentication_method = "azure_ad_integrated"
tenant_id = "your-tenant-id"

[connections.local]
server = "localhost"
database = "devdb"
authentication_method = "sql"
username = "sa"
password = "${SA_PASSWORD}"
trust_server_cert = true

Tip: If both files exist, the project-level dbmcp.toml takes precedence and the user-level file is ignored entirely.

Using named connections

Once configured, pass the connection name to connect_database instead of individual parameters:

connect_database(connection_name="dev")

Explicit parameters override config values, so you can use a named connection as a base and override specific fields:

connect_database(connection_name="dev", database="other_db")

Connection fields reference

Field	Type	Default	Description
server	string	(required)	SQL Server hostname or IP
database	string	(required)	Database name
port	int	1433	SQL Server port
authentication_method	string	"sql"	sql, windows, azure_ad, or azure_ad_integrated
username	string	—	For SQL or Azure AD auth
password	string	—	Supports ${ENV_VAR} references
trust_server_cert	bool	false	Trust server certificate without validation
connection_timeout	int	30	Connection timeout in seconds
tenant_id	string	—	Azure AD tenant ID (for azure_ad_integrated)

Environment variable references

Credential fields support ${VAR_NAME} syntax. Variables are resolved at connection time (not when the config is loaded), so the environment variable must be set when you call connect_database:

[connections.prod]
server = "prod-server"
database = "proddb"
password = "${PROD_DB_PASSWORD}"

Corporate MITM TLS Gateways (Databricks)

If your Databricks workspace is reached via a corporate TLS-rewriting gateway (e.g. Cloudflare Zero Trust), Python won't trust the gateway's CA by default (it ignores NODE_EXTRA_CA_CERTS). Set ca_bundle in your Databricks connection config to point at the gateway CA file:

[connections.databricks-prod]
dialect = "databricks"
host = "${DATABRICKS_HOST}"
http_path = "${DATABRICKS_HTTP_PATH}"
token = "${DATABRICKS_TOKEN}"
catalog = "main"
ca_bundle = "~/.ssl-certs/gateway-ca.pem"  # PEM with the gateway CA

Alternatively, set DBMCP_CA_BUNDLE=/path/to/ca.pem in your shell as a process-wide fallback (applies to every Databricks connection that doesn't set ca_bundle explicitly). Tilde and ${VAR} are both expanded. Precedence: explicit per-connection ca_bundle > URL ?ca_bundle= query param > DBMCP_CA_BUNDLE env > unset (connector falls back to certifi).

Point ca_bundle at the gateway CA file alone — dbmcp automatically merges it with certifi's bundle at connect time, so standard intermediates (DigiCert, etc.) remain trusted alongside the gateway root.

This setting is currently Databricks-only; MSSQL and generic dialects do not have an equivalent hook yet.

MCP Tools Reference

Tool	Description
connect_database	Connect to a SQL Server instance (Windows auth, SQL auth, or Azure AD)
list_schemas	List all schemas with table/view counts
list_tables	List tables with filtering, sorting, and pagination
get_table_schema	Get detailed table schema (columns, indexes, foreign keys)
get_sample_data	Retrieve sample rows from a table
execute_query	Execute read-only SQL queries (supports CTEs)
get_column_info	Get column-level statistics and value distributions
find_pk_candidates	Discover likely primary key columns via uniqueness analysis
find_fk_candidates	Infer potential foreign key relationships between tables

Development

uv sync --group dev
uv run pytest tests/
uv run ruff check src/

Project Structure

dbmcp/
  src/
    mcp_server/    # FastMCP server, tool definitions
    db/            # Connection, metadata, query execution, validation
    analysis/      # PK discovery, FK inference, column stats
    models/        # Data models (schema, relationship, analysis)
  tests/
    unit/
    integration/
    compliance/
    performance/
  specs/           # Feature specifications

License

MIT