MCP Tabular Data Analysis Server

Instructions

List all tables in a SQLite database.

Args:
    db_path: Path to SQLite database file

Returns:
    Dictionary containing table names and their schemas

Implementation Reference

• src/mcp_tabular/server.py:432-487 (handler)

  The @mcp.tool()-decorated function implementing the list_tables tool. Connects to SQLite DB, queries sqlite_master for table names, fetches PRAGMA table_info for schema and COUNT(*) for row counts per table.

  @mcp.tool()
  def list_tables(db_path: str) -> dict[str, Any]:
      """
      List all tables in a SQLite database.
      
      Args:
          db_path: Path to SQLite database file
      
      Returns:
          Dictionary containing table names and their schemas
      """
      path = _resolve_path(db_path)
      if not path.exists():
          raise FileNotFoundError(
              f"Database not found: {db_path}\n"
              f"Resolved to: {path}\n"
              f"Project root: {_PROJECT_ROOT}"
          )
      
      conn = sqlite3.connect(str(path))
      try:
          # Get table names
          tables = pd.read_sql_query(
              "SELECT name FROM sqlite_master WHERE type='table'", conn
          )
          
          result = {"tables": {}}
          
          for table_name in tables["name"]:
              # Get schema for each table
              schema = pd.read_sql_query(
                  f"PRAGMA table_info({table_name})", conn
              )
              
              # Get row count
              count = pd.read_sql_query(
                  f"SELECT COUNT(*) as cnt FROM {table_name}", conn
              ).iloc[0]["cnt"]
              
              result["tables"][table_name] = {
                  "row_count": int(count),
                  "columns": [
                      {
                          "name": row["name"],
                          "type": row["type"],
                          "nullable": not row["notnull"],
                          "primary_key": bool(row["pk"]),
                      }
                      for _, row in schema.iterrows()
                  ]
              }
          
          return result
      finally:
          conn.close()